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Liang C, Pan S, Wu W, Chen F, Zhang C, Zhou C, Gao Y, Ruan X, Quan S, Zhao Q, Pan J. Glucocorticoid therapy for sepsis in the AI era: a survey on current and future approaches. Comput Struct Biotechnol J 2024; 24:292-305. [PMID: 38681133 PMCID: PMC11047203 DOI: 10.1016/j.csbj.2024.04.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 04/06/2024] [Accepted: 04/07/2024] [Indexed: 05/01/2024] Open
Abstract
Sepsis, a life-threatening medical condition, manifests as new or worsening organ failures due to a dysregulated host response to infection. Many patients with sepsis have manifested a hyperinflammatory phenotype leading to the identification of inflammatory modulation by corticosteroids as a key treatment modality. However, the optimal use of corticosteroids in sepsis treatment remains a contentious subject, necessitating a deeper understanding of their physiological and pharmacological effects. Our study conducts a comprehensive review of randomized controlled trials (RCTs) focusing on traditional corticosteroid treatment in sepsis, alongside an analysis of evolving clinical guidelines. Additionally, we explore the emerging role of artificial intelligence (AI) in medicine, particularly in diagnosing, prognosticating, and treating sepsis. AI's advanced data processing capabilities reveal new avenues for enhancing corticosteroid therapeutic strategies in sepsis. The integration of AI in sepsis treatment has the potential to address existing gaps in knowledge, especially in the application of corticosteroids. Our findings suggest that combining corticosteroid therapy with AI-driven insights could lead to more personalized and effective sepsis treatments. This approach holds promise for improving clinical outcomes and presents a significant advancement in the management of this complex and often fatal condition.
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Affiliation(s)
- Chenglong Liang
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
- Wenzhou Medical University, Wenzhou 325000, China
- School of Nursing, Wenzhou Medical University, Wenzhou 325000, China
| | - Shuo Pan
- Wenzhou Medical University, Wenzhou 325000, China
| | - Wei Wu
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan 114051, China
| | - Fanxuan Chen
- Wenzhou Medical University, Wenzhou 325000, China
- School of Biomedical Engineering, School of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou 325000, China
| | - Chengxi Zhang
- School of Materials Science and Engineering, Shandong Jianzhu University, Jinan 250101, China
| | - Chen Zhou
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Yifan Gao
- Department of Intensive Care Unit, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Xiangyuan Ruan
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Shichao Quan
- Department of Big Data in Health Science, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan 114051, China
| | - Jingye Pan
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
- Department of Intensive Care Unit, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
- Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Province, Wenzhou 325000, China
- Wenzhou Key Laboratory of Critical Care and Artificial Intelligence, Wenzhou 325000, China
- Zhejiang Engineering Research Center for Hospital Emergency and Process Digitization, Wenzhou 325000, China
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2
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Safdar M, Ullah M, Hamayun S, Wahab A, Khan SU, Abdikakhorovich SA, Haq ZU, Mehreen A, Naeem M, Mustopa AZ, Hasan N. Microbiome miracles and their pioneering advances and future frontiers in cardiovascular disease. Curr Probl Cardiol 2024; 49:102686. [PMID: 38830479 DOI: 10.1016/j.cpcardiol.2024.102686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Accepted: 05/28/2024] [Indexed: 06/05/2024]
Abstract
Cardiovascular diseases (CVDs) represent a significant global health challenge, underscoring the need for innovative approaches to prevention and treatment. Recent years have seen a surge in interest in unraveling the complex relationship between the gut microbiome and cardiovascular health. This article delves into current research on the composition, diversity, and impact of the gut microbiome on CVD development. Recent advancements have elucidated the profound influence of the gut microbiome on disease progression, particularly through key mediators like Trimethylamine-N-oxide (TMAO) and other microbial metabolites. Understanding these mechanisms reveals promising therapeutic targets, including interventions aimed at modulating the gut microbiome's interaction with the immune system and its contribution to endothelial dysfunction. Harnessing this understanding, personalized medicine strategies tailored to individuals' gut microbiome profiles offer innovative avenues for reducing cardiovascular risk. As research in this field continues to evolve, there is vast potential for transformative advancements in cardiovascular medicine, paving the way for precision prevention and treatment strategies to address this global health challenge.
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Affiliation(s)
- Mishal Safdar
- Department of Biological Sciences, National University of Medical Sciences (NUMS), Rawalpindi, Punjab, Pakistan
| | - Muneeb Ullah
- College of Pharmacy, Pusan National University, Busandaehak-ro 63 beon-gil 2, Geumjeong-gu, Busan 46241, Republic of Korea; Department of Pharmacy, Kohat University of Science and Technology, Kohat, 26000, Khyber Pakhtunkhwa, Pakistan
| | - Shah Hamayun
- Department of Cardiology, Pakistan Institute of Medical Sciences (PIMS), Islamabad, 04485 Punjab, Pakistan
| | - Abdul Wahab
- Department of Pharmacy, Kohat University of Science and Technology, Kohat, 26000, Khyber Pakhtunkhwa, Pakistan
| | - Shahid Ullah Khan
- Department of Biochemistry, Women Medical and Dental College, Khyber Medical University, Abbottabad, 22080, Khyber Pakhtunkhwa, Pakistan
| | | | - Zia Ul Haq
- Department of Public Health, Institute of Public Health Sciences, Khyber Medical University, Peshawar 25120, Pakistan
| | - Aqsa Mehreen
- Department of Biological Sciences, National University of Medical Sciences (NUMS), Rawalpindi, Punjab, Pakistan
| | - Muhammad Naeem
- Department of Biological Sciences, National University of Medical Sciences (NUMS), Rawalpindi, Punjab, Pakistan
| | - Apon Zaenal Mustopa
- Research Center for Genetic Engineering, National Research, and Innovation Agency (BRIN), Bogor 16911, Indonesia
| | - Nurhasni Hasan
- Faculty of Pharmacy, Universitas Hasanuddin, Jl. Perintis Kemerdekaan Km 10, Makassar 90245, Republic of Indonesia.
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Ahmad F, Muhmood T. Clinical translation of nanomedicine with integrated digital medicine and machine learning interventions. Colloids Surf B Biointerfaces 2024; 241:114041. [PMID: 38897022 DOI: 10.1016/j.colsurfb.2024.114041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 06/11/2024] [Accepted: 06/13/2024] [Indexed: 06/21/2024]
Abstract
Nanomaterials based therapeutics transform the ways of disease prevention, diagnosis and treatment with increasing sophistications in nanotechnology at a breakneck pace, but very few could reach to the clinic due to inconsistencies in preclinical studies followed by regulatory hinderances. To tackle this, integrating the nanomedicine discovery with digital medicine provide technologies as tools of specific biological activity measurement. Hence, overcome the redundancies in nanomedicine discovery by the on-site data acquisition and analytics through integrating intelligent sensors and artificial intelligence (AI) or machine learning (ML). Integrated AI/ML wearable sensors directly gather clinically relevant biochemical information from the subject's body and process data for physicians to make right clinical decision(s) in a time and cost-effective way. This review summarizes insights and recommend the infusion of actionable big data computation enabled sensors in burgeoning field of nanomedicine at academia, research institutes, and pharmaceutical industries, with a potential of clinical translation. Furthermore, many blind spots are present in modern clinically relevant computation, one of which could prevent ML-guided low-cost new nanomedicine development from being successfully translated into the clinic was also discussed.
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Affiliation(s)
- Farooq Ahmad
- State Key Laboratory of Chemistry and Utilization of Carbon Based Energy Resources, College of Chemistry, Xinjiang University, Urumqi 830017, China.
| | - Tahir Muhmood
- International Iberian Nanotechnology Laboratory (INL), Avenida Mestre José Veiga, Braga 4715-330, Portugal.
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Chen Z, Liang N, Li H, Zhang H, Li H, Yan L, Hu Z, Chen Y, Zhang Y, Wang Y, Ke D, Shi N. Exploring explainable AI features in the vocal biomarkers of lung disease. Comput Biol Med 2024; 179:108844. [PMID: 38981214 DOI: 10.1016/j.compbiomed.2024.108844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 05/15/2024] [Accepted: 06/04/2024] [Indexed: 07/11/2024]
Abstract
This review delves into the burgeoning field of explainable artificial intelligence (XAI) in the detection and analysis of lung diseases through vocal biomarkers. Lung diseases, often elusive in their early stages, pose a significant public health challenge. Recent advancements in AI have ushered in innovative methods for early detection, yet the black-box nature of many AI models limits their clinical applicability. XAI emerges as a pivotal tool, enhancing transparency and interpretability in AI-driven diagnostics. This review synthesizes current research on the application of XAI in analyzing vocal biomarkers for lung diseases, highlighting how these techniques elucidate the connections between specific vocal features and lung pathology. We critically examine the methodologies employed, the types of lung diseases studied, and the performance of various XAI models. The potential for XAI to aid in early detection, monitor disease progression, and personalize treatment strategies in pulmonary medicine is emphasized. Furthermore, this review identifies current challenges, including data heterogeneity and model generalizability, and proposes future directions for research. By offering a comprehensive analysis of explainable AI features in the context of lung disease detection, this review aims to bridge the gap between advanced computational approaches and clinical practice, paving the way for more transparent, reliable, and effective diagnostic tools.
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Affiliation(s)
- Zhao Chen
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ning Liang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Haoyuan Li
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Haili Zhang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Huizhen Li
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Lijiao Yan
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ziteng Hu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yaxin Chen
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yujing Zhang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yanping Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Dandan Ke
- Special Disease Clinic, Huaishuling Branch of Beijing Fengtai Hospital of Integrated Traditional Chinese and Western Medicine, Beijing, China.
| | - Nannan Shi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
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5
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Kong HJ, Kim YL. Application of artificial intelligence in dental crown prosthesis: a scoping review. BMC Oral Health 2024; 24:937. [PMID: 39138474 DOI: 10.1186/s12903-024-04657-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Accepted: 07/23/2024] [Indexed: 08/15/2024] Open
Abstract
BACKGROUND In recent years, artificial intelligence (AI) has made remarkable advancements and achieved significant accomplishments across the entire field of dentistry. Notably, efforts to apply AI in prosthodontics are continually progressing. This scoping review aims to present the applications and performance of AI in dental crown prostheses and related topics. METHODS We conducted a literature search of PubMed, Scopus, Web of Science, Google Scholar, and IEEE Xplore databases from January 2010 to January 2024. The included articles addressed the application of AI in various aspects of dental crown treatment, including fabrication, assessment, and prognosis. RESULTS The initial electronic literature search yielded 393 records, which were reduced to 315 after eliminating duplicate references. The application of inclusion criteria led to analysis of 12 eligible publications in the qualitative review. The AI-based applications included in this review were related to detection of dental crown finish line, evaluation of AI-based color matching, evaluation of crown preparation, evaluation of dental crown designed by AI, identification of a dental crown in an intraoral photo, and prediction of debonding probability. CONCLUSIONS AI has the potential to increase efficiency in processes such as fabricating and evaluating dental crowns, with a high level of accuracy reported in most of the analyzed studies. However, a significant number of studies focused on designing crowns using AI-based software, and these studies had a small number of patients and did not always present their algorithms. Standardized protocols for reporting and evaluating AI studies are needed to increase the evidence and effectiveness.
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Affiliation(s)
- Hyun-Jun Kong
- Department of Prosthodontics and Wonkwang Dental Research Institute, School of Dentistry, Wonkwang University, Iksan, Republic of Korea.
| | - Yu-Lee Kim
- Department of Prosthodontics, School of Dentistry, Wonkwang University, Iksan, Republic of Korea
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6
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Kafetzis I, Fuchs KH, Sodmann P, Troya J, Zoller W, Meining A, Hann A. Efficient artificial intelligence-based assessment of the gastroesophageal valve with Hill classification through active learning. Sci Rep 2024; 14:18825. [PMID: 39138220 DOI: 10.1038/s41598-024-68866-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 07/29/2024] [Indexed: 08/15/2024] Open
Abstract
Standardized assessment of the gastroesophageal valve during endoscopy, attainable via the Hill classification, is important for clinical assessment and therapeutic decision making. The Hill classification is associated with the presence of hiatal hernia (HH), a common endoscopic finding connected to gastro-esophageal reflux disease. A novel efficient medical artificial intelligence (AI) training pipeline using active learning (AL) is designed. We identified 21,970 gastroscopic images as training data and used our AL to train a model for predicting the Hill classification and detecting HH. Performance of the AL and traditionally trained models were evaluated on an external expert-annotated image collection. The AL model achieved accuracy of 76%. A traditionally trained model with 125% more training data achieved 77% accuracy. Furthermore, the AL model achieved higher precision than the traditional one for rare classes, with 0.54 versus 0.39 (p < 0.05) for grade 3 and 0.72 versus 0.61 (p < 0.05) for grade 4. In detecting HH, the AL model achieved 94% accuracy, 0.72 precision and 0.74 recall. Our AL pipeline is more efficient than traditional methods in training AI for endoscopy.
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Affiliation(s)
- Ioannis Kafetzis
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine 2, University Hospital Würzburg, Oberdürrbacherstr. 6, 97080, Würzburg, Germany.
| | - Karl-Hermann Fuchs
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine 2, University Hospital Würzburg, Oberdürrbacherstr. 6, 97080, Würzburg, Germany
| | - Philipp Sodmann
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine 2, University Hospital Würzburg, Oberdürrbacherstr. 6, 97080, Würzburg, Germany
| | - Joel Troya
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine 2, University Hospital Würzburg, Oberdürrbacherstr. 6, 97080, Würzburg, Germany
| | - Wolfram Zoller
- Clinic for General Internal Medicine, Gastroenterology, Hepatology and Infectiology, Pneumology, Klinikum Stuttgart-Katharinenhospital, Kriegsbergstr. 60, 70174, Stuttgart, Germany
| | - Alexander Meining
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine 2, University Hospital Würzburg, Oberdürrbacherstr. 6, 97080, Würzburg, Germany
| | - Alexander Hann
- Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine 2, University Hospital Würzburg, Oberdürrbacherstr. 6, 97080, Würzburg, Germany
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7
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Rubulotta F, Bahrami S, Marshall DC, Komorowski M. Machine Learning Tools for Acute Respiratory Distress Syndrome Detection and Prediction. Crit Care Med 2024:00003246-990000000-00361. [PMID: 39133071 DOI: 10.1097/ccm.0000000000006390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Machine learning (ML) tools for acute respiratory distress syndrome (ARDS) detection and prediction are increasingly used. Therefore, understanding risks and benefits of such algorithms is relevant at the bedside. ARDS is a complex and severe lung condition that can be challenging to define precisely due to its multifactorial nature. It often arises as a response to various underlying medical conditions, such as pneumonia, sepsis, or trauma, leading to widespread inflammation in the lungs. ML has shown promising potential in supporting the recognition of ARDS in ICU patients. By analyzing a variety of clinical data, including vital signs, laboratory results, and imaging findings, ML models can identify patterns and risk factors associated with the development of ARDS. This detection and prediction could be crucial for timely interventions, diagnosis and treatment. In summary, leveraging ML for the early prediction and detection of ARDS in ICU patients holds great potential to enhance patient care, improve outcomes, and contribute to the evolving landscape of precision medicine in critical care settings. This article is a concise definitive review on artificial intelligence and ML tools for the prediction and detection of ARDS in critically ill patients.
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Affiliation(s)
- Francesca Rubulotta
- Department of Critical Care Medicine, McGill University, Montreal, QC, Canada
| | - Sahar Bahrami
- Department of Critical Care Medicine, McGill University, Montreal, QC, Canada
| | - Dominic C Marshall
- Division of Anaesthetics, Pain Medicine, and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Matthieu Komorowski
- Division of Anaesthetics, Pain Medicine, and Intensive Care, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
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Illingworth PJ, Venetis C, Gardner DK, Nelson SM, Berntsen J, Larman MG, Agresta F, Ahitan S, Ahlström A, Cattrall F, Cooke S, Demmers K, Gabrielsen A, Hindkjær J, Kelley RL, Knight C, Lee L, Lahoud R, Mangat M, Park H, Price A, Trew G, Troest B, Vincent A, Wennerström S, Zujovic L, Hardarson T. Deep learning versus manual morphology-based embryo selection in IVF: a randomized, double-blind noninferiority trial. Nat Med 2024:10.1038/s41591-024-03166-5. [PMID: 39122964 DOI: 10.1038/s41591-024-03166-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 06/29/2024] [Indexed: 08/12/2024]
Abstract
To assess the value of deep learning in selecting the optimal embryo for in vitro fertilization, a multicenter, randomized, double-blind, noninferiority parallel-group trial was conducted across 14 in vitro fertilization clinics in Australia and Europe. Women under 42 years of age with at least two early-stage blastocysts on day 5 were randomized to either the control arm, using standard morphological assessment, or the study arm, employing a deep learning algorithm, intelligent Data Analysis Score (iDAScore), for embryo selection. The primary endpoint was a clinical pregnancy rate with a noninferiority margin of 5%. The trial included 1,066 patients (533 in the iDAScore group and 533 in the morphology group). The iDAScore group exhibited a clinical pregnancy rate of 46.5% (248 of 533 patients), compared to 48.2% (257 of 533 patients) in the morphology arm (risk difference -1.7%; 95% confidence interval -7.7, 4.3; P = 0.62). This study was not able to demonstrate noninferiority of deep learning for clinical pregnancy rate when compared to standard morphology and a predefined prioritization scheme. Australian New Zealand Clinical Trials Registry (ANZCTR) registration: 379161 .
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Affiliation(s)
| | - Christos Venetis
- IVFAustralia, Sydney, New South Wales, Australia
- Unit for Human Reproduction, 1st Dept of Ob/Gyn, Medical School, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Centre for Big Data Research in Health, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
| | - David K Gardner
- Melbourne IVF, Melbourne, Victoria, Australia
- School of BioSciences, University of Melbourne, Parkville, Victoria, Australia
| | - Scott M Nelson
- School of Medicine, Dentistry and Nursing, University of Glasgow, Glasgow, UK
- TFP Fertility, Institute of Reproductive Sciences, Oxford, UK
| | | | | | | | | | - Aisling Ahlström
- IVIRMA Global Research Alliance, Livio Gothenburg, Gothenburg, Sweden
| | | | - Simon Cooke
- IVFAustralia, Sydney, New South Wales, Australia
| | - Kristy Demmers
- Queensland Fertility Group, Brisbane, Queensland, Australia
| | | | | | | | | | - Lisa Lee
- Melbourne IVF, Melbourne, Victoria, Australia
| | | | | | - Hannah Park
- Dept of Reproductive Medicine, Sahlgrenska University Hospital, Gothenburg, Sweden
| | | | - Geoffrey Trew
- TFP Fertility, Institute of Reproductive Sciences, Oxford, UK
- Imperial College London, London, UK
| | - Bettina Troest
- The Fertility Unit, Aalborg University Hospital, Aalborg, Denmark
| | - Anna Vincent
- TFP Fertility, Institute of Reproductive Sciences, Oxford, UK
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Nair M, Svedberg P, Larsson I, Nygren JM. A comprehensive overview of barriers and strategies for AI implementation in healthcare: Mixed-method design. PLoS One 2024; 19:e0305949. [PMID: 39121051 PMCID: PMC11315296 DOI: 10.1371/journal.pone.0305949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Accepted: 06/07/2024] [Indexed: 08/11/2024] Open
Abstract
Implementation of artificial intelligence systems for healthcare is challenging. Understanding the barriers and implementation strategies can impact their adoption and allows for better anticipation and planning. This study's objective was to create a detailed inventory of barriers to and strategies for AI implementation in healthcare to support advancements in methods and implementation processes in healthcare. A sequential explanatory mixed method design was used. Firstly, scoping reviews and systematic literature reviews were identified using PubMed. Selected studies included empirical cases of AI implementation and use in clinical practice. As the reviews were deemed insufficient to fulfil the aim of the study, data collection shifted to the primary studies included in those reviews. The primary studies were screened by title and abstract, and thereafter read in full text. Then, data on barriers to and strategies for AI implementation were extracted from the included articles, thematically coded by inductive analysis, and summarized. Subsequently, a direct qualitative content analysis of 69 interviews with healthcare leaders and healthcare professionals confirmed and added results from the literature review. Thirty-eight empirical cases from the six identified scoping and literature reviews met the inclusion and exclusion criteria. Barriers to and strategies for AI implementation were grouped under three phases of implementation (planning, implementing, and sustaining the use) and were categorized into eleven concepts; Leadership, Buy-in, Change management, Engagement, Workflow, Finance and human resources, Legal, Training, Data, Evaluation and monitoring, Maintenance. Ethics emerged as a twelfth concept through qualitative analysis of the interviews. This study illustrates the inherent challenges and useful strategies in implementing AI in healthcare practice. Future research should explore various aspects of leadership, collaboration and contracts among key stakeholders, legal strategies surrounding clinicians' liability, solutions to ethical dilemmas, infrastructure for efficient integration of AI in workflows, and define decision points in the implementation process.
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Affiliation(s)
- Monika Nair
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Petra Svedberg
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Ingrid Larsson
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Jens M. Nygren
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
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10
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Hurd TC, Cobb Payton F, Hood DB. Targeting Machine Learning and Artificial Intelligence Algorithms in Health Care to Reduce Bias and Improve Population Health. Milbank Q 2024. [PMID: 39116187 DOI: 10.1111/1468-0009.12712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 05/31/2024] [Accepted: 07/10/2024] [Indexed: 08/10/2024] Open
Abstract
Policy Points Artificial intelligence (AI) is disruptively innovating health care and surpassing our ability to define its boundaries and roles in health care and regulate its application in legal and ethical ways. Significant progress has been made in governance in the United States and the European Union. It is incumbent on developers, end users, the public, providers, health care systems, and policymakers to collaboratively ensure that we adopt a national AI health strategy that realizes the Quintuple Aim; minimizes race-based medicine; prioritizes transparency, equity, and algorithmic vigilance; and integrates the patient and community voices throughout all aspects of AI development and deployment.
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Affiliation(s)
- Thelma C Hurd
- Institute on Health Disparities, Equity, and the Exposome, Meharry Medical College
- School of Social Sciences, Humanities and Arts, University of California Merced
| | - Fay Cobb Payton
- School of Arts and Sciences, Rutgers University-Newark
- North Carolina State University
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11
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Nair G, Vedula A, Johnson ET, Thomas J, Patel R, Cheng J, Vedula R. Combining Image similarity and Predictive AI Models to Decrease Subjectivity in Thyroid Nodule Diagnosis and Improve Malignancy Prediction. Endocr Pract 2024:S1530-891X(24)00647-5. [PMID: 39127110 DOI: 10.1016/j.eprac.2024.08.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 08/01/2024] [Accepted: 08/05/2024] [Indexed: 08/12/2024]
Abstract
OBJECTIVES To evaluate the efficacy of combining predictive artificial intelligence (AI) and image similarity model to risk stratify thyroid nodules, using retrospective external validation study. METHODS Two datasets were used to determine efficacy of the AI application. One was Stanford dataset ultrasound images of 192 nodules between April 2017 to May 2018 and the second was private practice consisting of 118 thyroid nodule images between January 2018 to December 2023. The nodules had definitive diagnosis by cytology or surgical pathology. The AI application was used to predict the diagnosis and American College of Radiology Thyroid Imaging and Data System (ACR TI-RADS) score. RESULTS In the Stanford dataset, the AI application predicted malignancies with sensitivity of 1.0 and specificity of 0.55. Positive predictive value (PPV) was 0.18 and negative predictive value (NPV) was 1.0. The Area Under the Curve - Receiver Operating Characteristic (AUC-ROC) was 0.78. ACR TI-RADS based clinical recommendation had a polychoric correlation of 0.67. In the private dataset, the AI application predicted malignancies with sensitivity of 0.91 and specificity of 0.95. PPV was 0.8 and NPV was 0.98. AUC-ROC was 0.93 and accuracy was 0.94. ACR TI-RADS based score had a polychoric correlation of 0.94. CONCLUSION The AI application showed good performance for sensitivity and NPV between the two datasets and demonstrated potential for 61.5% reduction in the need for fine needle aspiration (FNA) and strong correlation to ACR TI-RADS. However, PPV was variable between the datasets possibly from variability in image selection and prevalence of malignancy. If implemented widely and consistently among various clinical settings, this could lead to decreased patient burden associated with an invasive procedure and possibly to decreased health care spending.
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Affiliation(s)
- Govind Nair
- Saint Louis University Medical Scholars Program, Saint Louis University, Saint Louis, MO
| | | | | | - Johnson Thomas
- Saint Louis University, St. Louis, Missouri, Department of Endocrinology, Mercy Hospital, Springfield, MO
| | - Rajshree Patel
- Endocrinology, Diabetes and Metabolism, Princeton Medical Group, Princeton, NJ
| | - Jennifer Cheng
- Division Chief of Endocrinology, HMH Jersey Shore University Medical Center, Hackensack Meridian School of Medicine, Neptune, NJ
| | - Ramya Vedula
- Assistant Professor of Clinical Medicine, Robert Wood Johnson School of Medicine, New Brunswick, NJ.
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Hatherley J. Are clinicians ethically obligated to disclose their use of medical machine learning systems to patients? JOURNAL OF MEDICAL ETHICS 2024:jme-2024-109905. [PMID: 39117396 DOI: 10.1136/jme-2024-109905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 07/26/2024] [Indexed: 08/10/2024]
Abstract
It is commonly accepted that clinicians are ethically obligated to disclose their use of medical machine learning systems to patients, and that failure to do so would amount to a moral fault for which clinicians ought to be held accountable. Call this 'the disclosure thesis.' Four main arguments have been, or could be, given to support the disclosure thesis in the ethics literature: the risk-based argument, the rights-based argument, the materiality argument and the autonomy argument. In this article, I argue that each of these four arguments are unconvincing, and therefore, that the disclosure thesis ought to be rejected. I suggest that mandating disclosure may also even risk harming patients by providing stakeholders with a way to avoid accountability for harm that results from improper applications or uses of these systems.
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Affiliation(s)
- Joshua Hatherley
- Department of Philosophy and History of Ideas, Aarhus University, Aarhus, Denmark
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13
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Yi H, Ou-Yang X, Hong Q, Liu L, Liu M, Wang Y, Zhang G, Ma F, Mu J, Mao Y. Patient-reported outcomes in lung cancer surgery: A narrative review. Asian J Surg 2024:S1015-9584(24)01677-4. [PMID: 39117541 DOI: 10.1016/j.asjsur.2024.07.304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Revised: 07/17/2024] [Accepted: 07/25/2024] [Indexed: 08/10/2024] Open
Abstract
Lung cancer is a leading cause of cancer-related mortality worldwide, profoundly affecting patients' quality of life. Patient-reported outcomes (PROs) provide essential insights from the patients' perspective, a crucial aspect often overlooked by traditional clinical outcomes. This review synthesizes research on the role of PROs in lung cancer surgery to enhance patient care and outcomes. We conducted a comprehensive literature search across PubMed, Scopus, and Web of Science up to March 2024, using terms such as "lung cancer," "Patient Reported Outcome," "lobectomy," "segmentectomy," and "lung surgery." The criteria included original studies on lung cancer patients who underwent surgical treatment and reported on PROs. After screening and removing duplicates, reviews, non-English articles, and irrelevant studies, 36 research articles were selected, supported by an additional 53 publications, totaling 89 references. The findings highlight the utility of PROs in assessing post-surgical outcomes, informing clinical decisions, and facilitating patient-centered care. However, challenges in standardization, patient burden, and integration into clinical workflows remain, underscoring the need for further research and methodological refinement. PROs are indispensable for understanding the quality-of-life post-surgery and enhancing communication and decision-making in clinical practice. Their integration into routine care is vital for a holistic approach to lung cancer treatment, promising significant improvements in patient outcomes and quality of care.
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Affiliation(s)
- Hang Yi
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xu Ou-Yang
- Shantou University Medical College, Shantou, 515041, China
| | - Qian Hong
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Lu Liu
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Man Liu
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yan Wang
- The Johns Hopkins University, Bloomberg School of Public Health, Epidemiology, Baltimore, MD, USA
| | - Guochao Zhang
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Fengyan Ma
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Juwei Mu
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Yousheng Mao
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
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Wang J, Liu G, Zhou C, Cui X, Wang W, Wang J, Huang Y, Jiang J, Wang Z, Tang Z, Zhang A, Cui D. Application of artificial intelligence in cancer diagnosis and tumor nanomedicine. NANOSCALE 2024; 16:14213-14246. [PMID: 39021117 DOI: 10.1039/d4nr01832j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Cancer is a major health concern due to its high incidence and mortality rates. Advances in cancer research, particularly in artificial intelligence (AI) and deep learning, have shown significant progress. The swift evolution of AI in healthcare, especially in tools like computer-aided diagnosis, has the potential to revolutionize early cancer detection. This technology offers improved speed, accuracy, and sensitivity, bringing a transformative impact on cancer diagnosis, treatment, and management. This paper provides a concise overview of the application of artificial intelligence in the realms of medicine and nanomedicine, with a specific emphasis on the significance and challenges associated with cancer diagnosis. It explores the pivotal role of AI in cancer diagnosis, leveraging structured, unstructured, and multimodal fusion data. Additionally, the article delves into the applications of AI in nanomedicine sensors and nano-oncology drugs. The fundamentals of deep learning and convolutional neural networks are clarified, underscoring their relevance to AI-driven cancer diagnosis. A comparative analysis is presented, highlighting the accuracy and efficiency of traditional methods juxtaposed with AI-based approaches. The discussion not only assesses the current state of AI in cancer diagnosis but also delves into the challenges faced by AI in this context. Furthermore, the article envisions the future development direction and potential application of artificial intelligence in cancer diagnosis, offering a hopeful prospect for enhanced cancer detection and improved patient prognosis.
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Affiliation(s)
- Junhao Wang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Guan Liu
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Cheng Zhou
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Xinyuan Cui
- Imaging Department of Rui Jin Hospital, Medical School of Shanghai Jiao Tong University, Shanghai, China
| | - Wei Wang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Jiulin Wang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Yixin Huang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Jinlei Jiang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Zhitao Wang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Zengyi Tang
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Amin Zhang
- Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China.
| | - Daxiang Cui
- School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
- School of Medicine, Henan University, Henan, China
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15
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Fitzek S, Choi KEA. Shaping future practices: German-speaking medical and dental students' perceptions of artificial intelligence in healthcare. BMC MEDICAL EDUCATION 2024; 24:844. [PMID: 39107732 PMCID: PMC11304766 DOI: 10.1186/s12909-024-05826-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 07/26/2024] [Indexed: 08/10/2024]
Abstract
BACKGROUND The growing use of artificial intelligence (AI) in healthcare necessitates understanding the perspectives of future practitioners. This study investigated the perceptions of German-speaking medical and dental students regarding the role of artificial intelligence (AI) in their future practices. METHODS A 28-item survey adapted from the AI in Healthcare Education Questionnaire (AIHEQ) and the Medical Student's Attitude Toward AI in Medicine (MSATAIM) scale was administered to students in Austria, Germany, and Switzerland from April to July 2023. Participants were recruited through targeted advertisements on Facebook and Instagram and were required to be proficient in German and enrolled in medical or dental programs. The data analysis included descriptive statistics, correlations, t tests, and thematic analysis of the open-ended responses. RESULTS Of the 409 valid responses (mean age = 23.13 years), only 18.2% of the participants reported receiving formal training in AI. Significant positive correlations were found between self-reported tech-savviness and AI familiarity (r = 0.67) and between confidence in finding reliable AI information and positive attitudes toward AI (r = 0.72). While no significant difference in AI familiarity was found between medical and dental students, dental students exhibited slightly more positive attitudes toward the integration of AI into their future practices. CONCLUSION This study underscores the need for comprehensive AI education in medical and dental curricula to address knowledge gaps and prepare future healthcare professionals for the ethical and effective integration of AI in practice.
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Affiliation(s)
- Sebastian Fitzek
- Health Services Research, Faculty of Medicine/Dentistry, Danube Private University, Steiner Landstraße 124, Krems‑Stein, 3500, Austria.
| | - Kyung-Eun Anna Choi
- Health Services Research, Faculty of Medicine/Dentistry, Danube Private University, Steiner Landstraße 124, Krems‑Stein, 3500, Austria
- Center for Health Services Research, Brandenburg Medical School, Seebad 82/83, 15562 Rüdersdorf b. Berlin, Neuruppin, Germany
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Geantă M, Bădescu D, Chirca N, Nechita OC, Radu CG, Rascu S, Rădăvoi D, Sima C, Toma C, Jinga V. The Potential Impact of Large Language Models on Doctor-Patient Communication: A Case Study in Prostate Cancer. Healthcare (Basel) 2024; 12:1548. [PMID: 39120251 PMCID: PMC11311818 DOI: 10.3390/healthcare12151548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Revised: 07/16/2024] [Accepted: 08/03/2024] [Indexed: 08/10/2024] Open
Abstract
BACKGROUND In recent years, the integration of large language models (LLMs) into healthcare has emerged as a revolutionary approach to enhancing doctor-patient communication, particularly in the management of diseases such as prostate cancer. METHODS Our paper evaluated the effectiveness of three prominent LLMs-ChatGPT (3.5), Gemini (Pro), and Co-Pilot (the free version)-against the official Romanian Patient's Guide on prostate cancer. Employing a randomized and blinded method, our study engaged eight medical professionals to assess the responses of these models based on accuracy, timeliness, comprehensiveness, and user-friendliness. RESULTS The primary objective was to explore whether LLMs, when operating in Romanian, offer comparable or superior performance to the Patient's Guide, considering their potential to personalize communication and enhance the informational accessibility for patients. Results indicated that LLMs, particularly ChatGPT, generally provided more accurate and user-friendly information compared to the Guide. CONCLUSIONS The findings suggest a significant potential for LLMs to enhance healthcare communication by providing accurate and accessible information. However, variability in performance across different models underscores the need for tailored implementation strategies. We highlight the importance of integrating LLMs with a nuanced understanding of their capabilities and limitations to optimize their use in clinical settings.
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Affiliation(s)
- Marius Geantă
- Department of Urology, “Carol Davila” University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd., 050474 Bucharest, Romania
- Center for Innovation in Medicine, 42J Theodor Pallady Bvd., 032266 Bucharest, Romania
- United Nations University—Maastricht Economic and Social Research Institute on Innovation and Technology, Boschstraat 24, 6211 AX Maastricht, The Netherlands
| | - Daniel Bădescu
- Department of Urology, “Carol Davila” University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd., 050474 Bucharest, Romania
- Department of Urology, “Prof. Dr. Th. Burghele” Clinical Hospital, 20 Panduri Str., 050659 Bucharest, Romania
| | - Narcis Chirca
- Department of Urology, “Carol Davila” University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd., 050474 Bucharest, Romania
- Department of Urology, “Prof. Dr. Th. Burghele” Clinical Hospital, 20 Panduri Str., 050659 Bucharest, Romania
| | - Ovidiu Cătălin Nechita
- Department of Urology, “Carol Davila” University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd., 050474 Bucharest, Romania
- Department of Urology, “Prof. Dr. Th. Burghele” Clinical Hospital, 20 Panduri Str., 050659 Bucharest, Romania
| | - Cosmin George Radu
- Department of Urology, “Prof. Dr. Th. Burghele” Clinical Hospital, 20 Panduri Str., 050659 Bucharest, Romania
| | - Stefan Rascu
- Department of Urology, “Carol Davila” University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd., 050474 Bucharest, Romania
- Department of Urology, “Prof. Dr. Th. Burghele” Clinical Hospital, 20 Panduri Str., 050659 Bucharest, Romania
| | - Daniel Rădăvoi
- Department of Urology, “Carol Davila” University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd., 050474 Bucharest, Romania
- Department of Urology, “Prof. Dr. Th. Burghele” Clinical Hospital, 20 Panduri Str., 050659 Bucharest, Romania
| | - Cristian Sima
- Department of Urology, “Carol Davila” University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd., 050474 Bucharest, Romania
- Department of Urology, “Prof. Dr. Th. Burghele” Clinical Hospital, 20 Panduri Str., 050659 Bucharest, Romania
| | - Cristian Toma
- Department of Urology, “Carol Davila” University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd., 050474 Bucharest, Romania
- Department of Urology, “Prof. Dr. Th. Burghele” Clinical Hospital, 20 Panduri Str., 050659 Bucharest, Romania
| | - Viorel Jinga
- Department of Urology, “Carol Davila” University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd., 050474 Bucharest, Romania
- Department of Urology, “Prof. Dr. Th. Burghele” Clinical Hospital, 20 Panduri Str., 050659 Bucharest, Romania
- Academy of Romanian Scientists, 3 Ilfov, 050085 Bucharest, Romania
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Nedadur R, Bhatt N, Lui T, Chu MWA, McCarthy PM, Kline A. The Emerging and Important Role of Artificial Intelligence in Cardiac Surgery. Can J Cardiol 2024:S0828-282X(24)00586-5. [PMID: 39098601 DOI: 10.1016/j.cjca.2024.07.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 07/29/2024] [Accepted: 07/29/2024] [Indexed: 08/06/2024] Open
Abstract
Artificial Intelligence (AI) has greatly affected our everyday lives and holds great promise to change the landscape of medicine. AI is particularly positioned to improve care for the increasingly complex patients undergoing cardiac surgery utilizing immense amount of data generated in the course of their care. When deployed, AI can be used to analyze this information at the patient's bedside more expediently and accurately, all while providing new insights. This review summarizes the current applications of AI in cardiac surgery, from the vantage point of a patient's journey. Applications of AI include pre-operative risk assessment, intraoperative planning, post-operative patient care and out-patient telemonitoring, encompassing the spectrum of cardiac surgical care. Offloading of administrative processes and enhanced experience with information gathering also represent a unique and underrepresented avenue for future utilization of AI. As clinicians, understanding the nomenclature and applications of AI is important to contextualize problems, to ensure problem-driven solutions and for clinical benefit. Precision medicine, and thus clinically relevant AI, remains dependent on data curation and warehousing to gather insights from large multicenter repositories while treating privacy with the utmost importance. AI tasks should not be siloed but rather holistically integrated into clinical workflow to retain context and relevance. As cardiac surgeons, AI allows us to look forward to a bright future of more efficient utilization of our clinical expertise toward high-level decision making and technical prowess.
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Affiliation(s)
- Rashmi Nedadur
- Feinberg School of Medicine, Division of Cardiac Surgery, Northwestern University, Chicago, Illinois, United States; Center for Artificial Intelligence, Bluhm Cardiovascular Institute, Northwestern Medicine, Chicago, Illinois, United States
| | - Nitish Bhatt
- Peter Munk Cardiac Center, Toronto General Hospital, Toronto, Ontario, Canada
| | - Tom Lui
- Feinberg School of Medicine, Division of Cardiac Surgery, Northwestern University, Chicago, Illinois, United States; Center for Artificial Intelligence, Bluhm Cardiovascular Institute, Northwestern Medicine, Chicago, Illinois, United States
| | | | - Patrick M McCarthy
- Feinberg School of Medicine, Division of Cardiac Surgery, Northwestern University, Chicago, Illinois, United States; Center for Artificial Intelligence, Bluhm Cardiovascular Institute, Northwestern Medicine, Chicago, Illinois, United States
| | - Adrienne Kline
- Feinberg School of Medicine, Division of Cardiac Surgery, Northwestern University, Chicago, Illinois, United States; Center for Artificial Intelligence, Bluhm Cardiovascular Institute, Northwestern Medicine, Chicago, Illinois, United States
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18
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Nietsch KS, Shrestha N, Mazudie Ndjonko LC, Ahmed W, Mejia MR, Zaidat B, Ren R, Duey AH, Li SQ, Kim JS, Hidden KA, Cho SK. Can Large Language Models (LLMs) Predict the Appropriate Treatment of Acute Hip Fractures in Older Adults? Comparing Appropriate Use Criteria With Recommendations From ChatGPT. J Am Acad Orthop Surg Glob Res Rev 2024; 8:01979360-202408000-00007. [PMID: 39137403 PMCID: PMC11319315 DOI: 10.5435/jaaosglobal-d-24-00206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Accepted: 06/16/2024] [Indexed: 08/15/2024]
Abstract
BACKGROUND Acute hip fractures are a public health problem affecting primarily older adults. Chat Generative Pretrained Transformer may be useful in providing appropriate clinical recommendations for beneficial treatment. OBJECTIVE To evaluate the accuracy of Chat Generative Pretrained Transformer (ChatGPT)-4.0 by comparing its appropriateness scores for acute hip fractures with the American Academy of Orthopaedic Surgeons (AAOS) Appropriate Use Criteria given 30 patient scenarios. "Appropriateness" indicates the unexpected health benefits of treatment exceed the expected negative consequences by a wide margin. METHODS Using the AAOS Appropriate Use Criteria as the benchmark, numerical scores from 1 to 9 assessed appropriateness. For each patient scenario, ChatGPT-4.0 was asked to assign an appropriate score for six treatments to manage acute hip fractures. RESULTS Thirty patient scenarios were evaluated for 180 paired scores. Comparing ChatGPT-4.0 with AAOS scores, there was a positive correlation for multiple cannulated screw fixation, total hip arthroplasty, hemiarthroplasty, and long cephalomedullary nails. Statistically significant differences were observed only between scores for long cephalomedullary nails. CONCLUSION ChatGPT-4.0 scores were not concordant with AAOS scores, overestimating the appropriateness of total hip arthroplasty, hemiarthroplasty, and long cephalomedullary nails, and underestimating the other three. ChatGPT-4.0 was inadequate in selecting an appropriate treatment deemed acceptable, most reasonable, and most likely to improve patient outcomes.
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Affiliation(s)
- Katrina S. Nietsch
- From the Icahn School of Medicine at Mount Sinai, New York, NY (Ms. Nietsch, Mr. Ahmed, Mr. Mejia, Mr. Zaidat, Ms. Ren, and Mr. Duey); the Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL (Ms. Shrestha); the Northwestern University, Chicago, IL (Ms. Mazudie Ndjonko); the PGY-6, Department of Orthopedic Surgery and Neurosurgery, Mount Sinai Hospital, New York, NY (Dr. Li); the Department of Orthopedics and Orthopedic Surgery, Mount Sinai Hospital, New York, NY (Dr. Kim); the Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN (Dr. Hidden); and the Department of Orthopedic Surgery and Neurosurgery, Mount Sinai Hospital, New York, NY (Dr. Cho)
| | - Nancy Shrestha
- From the Icahn School of Medicine at Mount Sinai, New York, NY (Ms. Nietsch, Mr. Ahmed, Mr. Mejia, Mr. Zaidat, Ms. Ren, and Mr. Duey); the Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL (Ms. Shrestha); the Northwestern University, Chicago, IL (Ms. Mazudie Ndjonko); the PGY-6, Department of Orthopedic Surgery and Neurosurgery, Mount Sinai Hospital, New York, NY (Dr. Li); the Department of Orthopedics and Orthopedic Surgery, Mount Sinai Hospital, New York, NY (Dr. Kim); the Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN (Dr. Hidden); and the Department of Orthopedic Surgery and Neurosurgery, Mount Sinai Hospital, New York, NY (Dr. Cho)
| | - Laura C. Mazudie Ndjonko
- From the Icahn School of Medicine at Mount Sinai, New York, NY (Ms. Nietsch, Mr. Ahmed, Mr. Mejia, Mr. Zaidat, Ms. Ren, and Mr. Duey); the Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL (Ms. Shrestha); the Northwestern University, Chicago, IL (Ms. Mazudie Ndjonko); the PGY-6, Department of Orthopedic Surgery and Neurosurgery, Mount Sinai Hospital, New York, NY (Dr. Li); the Department of Orthopedics and Orthopedic Surgery, Mount Sinai Hospital, New York, NY (Dr. Kim); the Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN (Dr. Hidden); and the Department of Orthopedic Surgery and Neurosurgery, Mount Sinai Hospital, New York, NY (Dr. Cho)
| | - Wasil Ahmed
- From the Icahn School of Medicine at Mount Sinai, New York, NY (Ms. Nietsch, Mr. Ahmed, Mr. Mejia, Mr. Zaidat, Ms. Ren, and Mr. Duey); the Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL (Ms. Shrestha); the Northwestern University, Chicago, IL (Ms. Mazudie Ndjonko); the PGY-6, Department of Orthopedic Surgery and Neurosurgery, Mount Sinai Hospital, New York, NY (Dr. Li); the Department of Orthopedics and Orthopedic Surgery, Mount Sinai Hospital, New York, NY (Dr. Kim); the Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN (Dr. Hidden); and the Department of Orthopedic Surgery and Neurosurgery, Mount Sinai Hospital, New York, NY (Dr. Cho)
| | - Mateo Restrepo Mejia
- From the Icahn School of Medicine at Mount Sinai, New York, NY (Ms. Nietsch, Mr. Ahmed, Mr. Mejia, Mr. Zaidat, Ms. Ren, and Mr. Duey); the Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL (Ms. Shrestha); the Northwestern University, Chicago, IL (Ms. Mazudie Ndjonko); the PGY-6, Department of Orthopedic Surgery and Neurosurgery, Mount Sinai Hospital, New York, NY (Dr. Li); the Department of Orthopedics and Orthopedic Surgery, Mount Sinai Hospital, New York, NY (Dr. Kim); the Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN (Dr. Hidden); and the Department of Orthopedic Surgery and Neurosurgery, Mount Sinai Hospital, New York, NY (Dr. Cho)
| | - Bashar Zaidat
- From the Icahn School of Medicine at Mount Sinai, New York, NY (Ms. Nietsch, Mr. Ahmed, Mr. Mejia, Mr. Zaidat, Ms. Ren, and Mr. Duey); the Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL (Ms. Shrestha); the Northwestern University, Chicago, IL (Ms. Mazudie Ndjonko); the PGY-6, Department of Orthopedic Surgery and Neurosurgery, Mount Sinai Hospital, New York, NY (Dr. Li); the Department of Orthopedics and Orthopedic Surgery, Mount Sinai Hospital, New York, NY (Dr. Kim); the Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN (Dr. Hidden); and the Department of Orthopedic Surgery and Neurosurgery, Mount Sinai Hospital, New York, NY (Dr. Cho)
| | - Renee Ren
- From the Icahn School of Medicine at Mount Sinai, New York, NY (Ms. Nietsch, Mr. Ahmed, Mr. Mejia, Mr. Zaidat, Ms. Ren, and Mr. Duey); the Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL (Ms. Shrestha); the Northwestern University, Chicago, IL (Ms. Mazudie Ndjonko); the PGY-6, Department of Orthopedic Surgery and Neurosurgery, Mount Sinai Hospital, New York, NY (Dr. Li); the Department of Orthopedics and Orthopedic Surgery, Mount Sinai Hospital, New York, NY (Dr. Kim); the Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN (Dr. Hidden); and the Department of Orthopedic Surgery and Neurosurgery, Mount Sinai Hospital, New York, NY (Dr. Cho)
| | - Akiro H. Duey
- From the Icahn School of Medicine at Mount Sinai, New York, NY (Ms. Nietsch, Mr. Ahmed, Mr. Mejia, Mr. Zaidat, Ms. Ren, and Mr. Duey); the Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL (Ms. Shrestha); the Northwestern University, Chicago, IL (Ms. Mazudie Ndjonko); the PGY-6, Department of Orthopedic Surgery and Neurosurgery, Mount Sinai Hospital, New York, NY (Dr. Li); the Department of Orthopedics and Orthopedic Surgery, Mount Sinai Hospital, New York, NY (Dr. Kim); the Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN (Dr. Hidden); and the Department of Orthopedic Surgery and Neurosurgery, Mount Sinai Hospital, New York, NY (Dr. Cho)
| | - Samuel Q. Li
- From the Icahn School of Medicine at Mount Sinai, New York, NY (Ms. Nietsch, Mr. Ahmed, Mr. Mejia, Mr. Zaidat, Ms. Ren, and Mr. Duey); the Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL (Ms. Shrestha); the Northwestern University, Chicago, IL (Ms. Mazudie Ndjonko); the PGY-6, Department of Orthopedic Surgery and Neurosurgery, Mount Sinai Hospital, New York, NY (Dr. Li); the Department of Orthopedics and Orthopedic Surgery, Mount Sinai Hospital, New York, NY (Dr. Kim); the Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN (Dr. Hidden); and the Department of Orthopedic Surgery and Neurosurgery, Mount Sinai Hospital, New York, NY (Dr. Cho)
| | - Jun S. Kim
- From the Icahn School of Medicine at Mount Sinai, New York, NY (Ms. Nietsch, Mr. Ahmed, Mr. Mejia, Mr. Zaidat, Ms. Ren, and Mr. Duey); the Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL (Ms. Shrestha); the Northwestern University, Chicago, IL (Ms. Mazudie Ndjonko); the PGY-6, Department of Orthopedic Surgery and Neurosurgery, Mount Sinai Hospital, New York, NY (Dr. Li); the Department of Orthopedics and Orthopedic Surgery, Mount Sinai Hospital, New York, NY (Dr. Kim); the Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN (Dr. Hidden); and the Department of Orthopedic Surgery and Neurosurgery, Mount Sinai Hospital, New York, NY (Dr. Cho)
| | - Krystin A. Hidden
- From the Icahn School of Medicine at Mount Sinai, New York, NY (Ms. Nietsch, Mr. Ahmed, Mr. Mejia, Mr. Zaidat, Ms. Ren, and Mr. Duey); the Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL (Ms. Shrestha); the Northwestern University, Chicago, IL (Ms. Mazudie Ndjonko); the PGY-6, Department of Orthopedic Surgery and Neurosurgery, Mount Sinai Hospital, New York, NY (Dr. Li); the Department of Orthopedics and Orthopedic Surgery, Mount Sinai Hospital, New York, NY (Dr. Kim); the Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN (Dr. Hidden); and the Department of Orthopedic Surgery and Neurosurgery, Mount Sinai Hospital, New York, NY (Dr. Cho)
| | - Samuel K. Cho
- From the Icahn School of Medicine at Mount Sinai, New York, NY (Ms. Nietsch, Mr. Ahmed, Mr. Mejia, Mr. Zaidat, Ms. Ren, and Mr. Duey); the Chicago Medical School, Rosalind Franklin University of Medicine and Science, North Chicago, IL (Ms. Shrestha); the Northwestern University, Chicago, IL (Ms. Mazudie Ndjonko); the PGY-6, Department of Orthopedic Surgery and Neurosurgery, Mount Sinai Hospital, New York, NY (Dr. Li); the Department of Orthopedics and Orthopedic Surgery, Mount Sinai Hospital, New York, NY (Dr. Kim); the Department of Orthopedic Surgery, Mayo Clinic, Rochester, MN (Dr. Hidden); and the Department of Orthopedic Surgery and Neurosurgery, Mount Sinai Hospital, New York, NY (Dr. Cho)
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19
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Hoti K, Weidmann AE. Encouraging dissemination of research on the use of artificial intelligence and related innovative technologies in clinical pharmacy practice and education: call for papers. Int J Clin Pharm 2024; 46:777-779. [PMID: 39046690 DOI: 10.1007/s11096-024-01777-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2024] [Accepted: 07/05/2024] [Indexed: 07/25/2024]
Affiliation(s)
- Kreshnik Hoti
- Division of Pharmacy, Department of Pharmacy Practice and Pharmaceutical Care, Faculty of Medicine, University of Pristina, Prishtina, Kosovo
| | - Anita Elaine Weidmann
- Innsbruck University, Innsbruck, Austria.
- International Journal of Clinical Pharmacy and Research Committee, European Society of Clinical Pharmacy, Chaam, The Netherlands.
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Rodoplu Solovchuk D. Advances in AI-assisted biochip technology for biomedicine. Biomed Pharmacother 2024; 177:116997. [PMID: 38943990 DOI: 10.1016/j.biopha.2024.116997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 06/13/2024] [Accepted: 06/15/2024] [Indexed: 07/01/2024] Open
Abstract
The integration of biochips with AI opened up new possibilities and is expected to revolutionize smart healthcare tools within the next five years. The combination of miniaturized, multi-functional, rapid, high-throughput sample processing and sensing capabilities of biochips, with the computational data processing and predictive power of AI, allows medical professionals to collect and analyze vast amounts of data quickly and efficiently, leading to more accurate and timely diagnoses and prognostic evaluations. Biochips, as smart healthcare devices, offer continuous monitoring of patient symptoms. Integrated virtual assistants have the potential to send predictive feedback to users and healthcare practitioners, paving the way for personalized and predictive medicine. This review explores the current state-of-the-art biochip technologies including gene-chips, organ-on-a-chips, and neural implants, and the diagnostic and therapeutic utility of AI-assisted biochips in medical practices such as cancer, diabetes, infectious diseases, and neurological disorders. Choosing the appropriate AI model for a specific biomedical application, and possible solutions to the current challenges are explored. Surveying advances in machine learning models for biochip functionality, this paper offers a review of biochips for the future of biomedicine, an essential guide for keeping up with trends in healthcare, while inspiring cross-disciplinary collaboration among biomedical engineering, medicine, and machine learning fields.
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Affiliation(s)
- Didem Rodoplu Solovchuk
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Zhunan, Miaoli 35053, Taiwan.
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21
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Merle DA, Heidinger A, Horwath-Winter J, List W, Bauer H, Weissensteiner M, Kraus-Füreder P, Mayrhofer-Reinhartshuber M, Kainz P, Steinwender G, Wedrich A. Automated Measurement and Three-Dimensional Fitting of Corneal Ulcerations and Erosions via AI-Based Image Analysis. Curr Eye Res 2024; 49:835-842. [PMID: 38689527 DOI: 10.1080/02713683.2024.2344197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 04/12/2024] [Indexed: 05/02/2024]
Abstract
PURPOSE Artificial intelligence (AI)-tools hold great potential to compensate for missing resources in health-care systems but often fail to be implemented in clinical routine. Intriguingly, no-code and low-code technologies allow clinicians to develop Artificial intelligence (AI)-tools without requiring in-depth programming knowledge. Clinician-driven projects allow to adequately identify and address real clinical needs and, therefore, hold superior potential for clinical implementation. In this light, this study aimed for the clinician-driven development of a tool capable of measuring corneal lesions relative to total corneal surface area and eliminating inaccuracies in two-dimensional measurements by three-dimensional fitting of the corneal surface. METHODS Standard slit-lamp photographs using a blue-light filter after fluorescein instillation taken during clinical routine were used to train a fully convolutional network to automatically detect the corneal white-to-white distance, the total fluorescent area and the total erosive area. Based on these values, the algorithm calculates the affected area relative to total corneal surface area and fits the area on a three-dimensional representation of the corneal surface. RESULTS The developed algorithm reached dice scores >0.9 for an automated measurement of the relative lesion size. Furthermore, only 25% of conventional manual measurements were within a ± 10% range of the ground truth. CONCLUSIONS The developed algorithm is capable of reliably providing exact values for corneal lesion sizes. Additionally, three-dimensional modeling of the corneal surface is essential for an accurate measurement of lesion sizes. Besides telemedicine applications, this approach harbors great potential for clinical trials where exact quantitative and observer-independent measurements are essential.
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Affiliation(s)
- David A Merle
- Department of Ophthalmology, Medical University of Graz, Graz, Austria
- Department for Ophthalmology, University Eye Clinic, Eberhard Karls University of Tübingen, Tübingen, Germany
- Institute for Ophthalmic Research, Department for Ophthalmology, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Astrid Heidinger
- Department of Ophthalmology, Medical University of Graz, Graz, Austria
| | | | - Wolfgang List
- Department of Ophthalmology, Medical University of Graz, Graz, Austria
| | - Heimo Bauer
- Department of Ophthalmology, Medical University of Graz, Graz, Austria
| | | | | | | | | | | | - Andreas Wedrich
- Department of Ophthalmology, Medical University of Graz, Graz, Austria
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Núñez R, Doña I, Cornejo-García JA. Predictive models and applicability of artificial intelligence-based approaches in drug allergy. Curr Opin Allergy Clin Immunol 2024; 24:189-194. [PMID: 38814733 DOI: 10.1097/aci.0000000000001002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024]
Abstract
PURPOSE OF REVIEW Drug allergy is responsible for a huge burden on public healthcare systems, representing in some instances a threat for patient's life. Diagnosis is complex due to the heterogeneity of clinical phenotypes and mechanisms involved, the limitations of in vitro tests, and the associated risk to in vivo tests. Predictive models, including those using recent advances in artificial intelligence, may circumvent these drawbacks, leading to an appropriate classification of patients and improving their management in clinical settings. RECENT FINDINGS Scores and predictive models to assess drug allergy development, including patient risk stratification, are scarce and usually apply logistic regression analysis. Over recent years, different methods encompassed under the general umbrella of artificial intelligence, including machine and deep learning, and artificial neural networks, are emerging as powerful tools to provide reliable and optimal models for clinical diagnosis, prediction, and precision medicine in different types of drug allergy. SUMMARY This review provides general concepts and current evidence supporting the potential utility of predictive models and artificial intelligence branches in drug allergy diagnosis.
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Affiliation(s)
- Rafael Núñez
- Allergy Research Group, Biomedical Research Institute of Malaga (IBIMA)-BIONAND Platform
| | - Inmaculada Doña
- Allergy Research Group, Biomedical Research Institute of Malaga (IBIMA)-BIONAND Platform
- Allergy Unit, Malaga Regional University Hospital, Malaga
- Inflammatory Diseases Network (RICORS, RD21/0002/0008, Instituto de Salud Carlos III), Málaga, Spain
| | - José Antonio Cornejo-García
- Allergy Research Group, Biomedical Research Institute of Malaga (IBIMA)-BIONAND Platform
- Allergy Unit, Malaga Regional University Hospital, Malaga
- Inflammatory Diseases Network (RICORS, RD21/0002/0008, Instituto de Salud Carlos III), Málaga, Spain
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23
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Zink A, Chernew ME, Neprash HT. How Should Medicare Pay for Artificial Intelligence? JAMA Intern Med 2024; 184:863-864. [PMID: 38805195 DOI: 10.1001/jamainternmed.2024.1648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
This Viewpoint examines artificial intelligence–enabled clinical services, existing payment structures, and the economics of artificial intelligence pricing.
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Affiliation(s)
- Anna Zink
- University of Chicago Booth School of Business, Chicago, Illinois
| | - Michael E Chernew
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts
| | - Hannah T Neprash
- Division of Health Policy and Management, School of Public Health, University of Minnesota, Minneapolis
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24
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Tozsin A, Ucmak H, Soyturk S, Aydin A, Gozen AS, Fahim MA, Güven S, Ahmed K. The Role of Artificial Intelligence in Medical Education: A Systematic Review. Surg Innov 2024; 31:415-423. [PMID: 38632898 DOI: 10.1177/15533506241248239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
BACKGROUND To examine the artificial intelligence (AI) tools currently being studied in modern medical education, and critically evaluate the level of validation and the quality of evidence presented in each individual study. METHODS This review (PROSPERO ID: CRD42023410752) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. A database search was conducted using PubMed, Embase, and Cochrane Library. Articles written in the English language between 2000 and March 2023 were reviewed retrospectively using the MeSH Terms "AI" and "medical education" A total of 4642 potentially relevant studies were found. RESULTS After a thorough screening process, 36 studies were included in the final analysis. These studies consisted of 26 quantitative studies and 10 studies investigated the development and validation of AI tools. When examining the results of studies in which Support vector machines (SVMs) were employed, it has demonstrated high accuracy in assessing students' experiences, diagnosing acute abdominal pain, classifying skilled and novice participants, and evaluating surgical training levels. Particularly in the comparison of surgical skill levels, it has achieved an accuracy rate of over 92%. CONCLUSION AI tools demonstrated effectiveness in improving practical skills, diagnosing diseases, and evaluating student performance. However, further research with rigorous validation is required to identify the most effective AI tools for medical education.
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Affiliation(s)
- Atinc Tozsin
- Department of Urology, Trakya University School of Medicine, Edirne, Turkey
| | - Harun Ucmak
- Department of Urology, Meram School of Medicine, Necmettin Erbakan University, Konya, Turkey
| | - Selim Soyturk
- Department of Urology, Meram School of Medicine, Necmettin Erbakan University, Konya, Turkey
| | - Abdullatif Aydin
- MRC Centre for Transplantation, Guy's Hospital, King's College London, London, UK
- Department of Urology, King's College Hospital NHS Foundation Trust, London, UK
| | | | - Maha Al Fahim
- Medical Education Department, Sheikh Khalifa Medical City, Abu Dhabi, UAE
| | - Selcuk Güven
- Department of Urology, Meram School of Medicine, Necmettin Erbakan University, Konya, Turkey
| | - Kamran Ahmed
- MRC Centre for Transplantation, Guy's Hospital, King's College London, London, UK
- Khalifa University, Abu Dhabi, UAE
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Hong N, Whittier DE, Glüer CC, Leslie WD. The potential role for artificial intelligence in fracture risk prediction. Lancet Diabetes Endocrinol 2024; 12:596-600. [PMID: 38942044 DOI: 10.1016/s2213-8587(24)00153-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 05/16/2024] [Accepted: 05/16/2024] [Indexed: 06/30/2024]
Abstract
Osteoporotic fractures are a major health challenge in older adults. Despite the availability of safe and effective therapies for osteoporosis, these therapies are underused in individuals at high risk for fracture, calling for better case-finding and fracture risk assessment strategies. Artificial intelligence (AI) and machine learning (ML) hold promise for enhancing identification of individuals at high risk for fracture by distilling useful features from high-dimensional data derived from medical records, imaging, and wearable devices. AI-ML could enable automated opportunistic screening for vertebral fractures and osteoporosis, home-based monitoring and intervention targeting lifestyle factors, and integration of multimodal features to leverage fracture prediction, ultimately aiding improved fracture risk assessment and individualised treatment. Optimism must be balanced with consideration for the explainability of AI-ML models, biases (including information inequity in numerically under-represented populations), model limitations, and net clinical benefit and workload impact. Clinical integration of AI-ML algorithms has the potential to transform osteoporosis management, offering a more personalised approach to reduce the burden of osteoporotic fractures.
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Affiliation(s)
- Namki Hong
- Department of Internal Medicine, Endocrine Research Institute, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea; Institute for Innovation in Digital Healthcare, Yonsei University Health System, Seoul, Korea.
| | - Danielle E Whittier
- McCaig Institute for Bone and Joint Health and Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Claus-C Glüer
- Section Biomedical Imaging, Department of Radiology and Neuroradiology, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
| | - William D Leslie
- Department of Internal Medicine, University of Manitoba, Winnipeg, MB, Canada
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Burti S, Banzato T, Coghlan S, Wodzinski M, Bendazzoli M, Zotti A. Artificial intelligence in veterinary diagnostic imaging: Perspectives and limitations. Res Vet Sci 2024; 175:105317. [PMID: 38843690 DOI: 10.1016/j.rvsc.2024.105317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Revised: 05/22/2024] [Accepted: 05/29/2024] [Indexed: 06/17/2024]
Abstract
The field of veterinary diagnostic imaging is undergoing significant transformation with the integration of artificial intelligence (AI) tools. This manuscript provides an overview of the current state and future prospects of AI in veterinary diagnostic imaging. The manuscript delves into various applications of AI across different imaging modalities, such as radiology, ultrasound, computed tomography, and magnetic resonance imaging. Examples of AI applications in each modality are provided, ranging from orthopaedics to internal medicine, cardiology, and more. Notable studies are discussed, demonstrating AI's potential for improved accuracy in detecting and classifying various abnormalities. The ethical considerations of using AI in veterinary diagnostics are also explored, highlighting the need for transparent AI development, accurate training data, awareness of the limitations of AI models, and the importance of maintaining human expertise in the decision-making process. The manuscript underscores the significance of AI as a decision support tool rather than a replacement for human judgement. In conclusion, this comprehensive manuscript offers an assessment of the current landscape and future potential of AI in veterinary diagnostic imaging. It provides insights into the benefits and challenges of integrating AI into clinical practice while emphasizing the critical role of ethics and human expertise in ensuring the wellbeing of veterinary patients.
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Affiliation(s)
- Silvia Burti
- Department of Animal Medicine, Production and Health, University of Padua, Viale dell'Università 16, Legnaro, 35020 Padua, Italy.
| | - Tommaso Banzato
- Department of Animal Medicine, Production and Health, University of Padua, Viale dell'Università 16, Legnaro, 35020 Padua, Italy
| | - Simon Coghlan
- School of Computing and Information Systems, Centre for AI and Digital Ethics, Australian Research Council Centre of Excellence for Automated Decision-Making and Society, University of Melbourne, 3052 Melbourne, Australia
| | - Marek Wodzinski
- Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Krakow, 30059 Kraków, Poland; Information Systems Institute, University of Applied Sciences - Western Switzerland (HES-SO Valais), 3960 Sierre, Switzerland
| | - Margherita Bendazzoli
- Department of Animal Medicine, Production and Health, University of Padua, Viale dell'Università 16, Legnaro, 35020 Padua, Italy
| | - Alessandro Zotti
- Department of Animal Medicine, Production and Health, University of Padua, Viale dell'Università 16, Legnaro, 35020 Padua, Italy
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Hammad Jaber Amin M, Abdelmonim Gasm Alseed Fadlalmoula GA, Awadalla Mohamed Elhassan Elmahi M, hatim Khalid Alrabee N, Hemmeda L, Haydar Awad M, Mustafa Ahmed GE, Abbasher Hussien Mohamed Ahmed K. Knowledge, attitude, and practice of artificial intelligence applications in medicine among physicians in Sudan: a national cross-sectional survey. Ann Med Surg (Lond) 2024; 86:4416-4421. [PMID: 39118720 PMCID: PMC11305753 DOI: 10.1097/ms9.0000000000002274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 06/04/2024] [Indexed: 08/10/2024] Open
Abstract
Background and aims Artificial intelligence (AI) has emerged as a rapidly developing tool within the medical landscape, globally aiding in diagnosis and healthcare management. However, its integration within healthcare systems remains varied across different regions. In Sudan, there exists a burgeoning interest in AI potential applications within medicine. This study aims to evaluate the knowledge, attitudes, and practices of AI applications in medicine among physicians in Sudan. Methods The authors conducted a web-based survey cross-sectional analytical study using an online questionnaire-based survey regarding demographic details, knowledge, attitudes, and practice of AI distributing through various e-mail listings and social media platforms. A sample of 825 Physicians including doctors in Sudan with different ranks and specialties were selected using the convenient non-probability sampling technique. Result Out of 825 Physicians, 666 (80.7%) of Physicians have previous knowledge about AI. However, only a small number 123 (14.9%) were taught about AI during their time in medical school, even fewer, just 120 (14.5%) had AI-related lessons in their training program. Regarding attitude, 675 (81.8%) agree that AI is very important in medicine, almost the same number, 681 (82.6%) support the idea of teaching AI in medical schools. Practically, 535 (64.8%) of doctors, think that should get special training in using AI tools in healthcare. Excitingly 651 (78.9%) of physicians are interested in working with AI in future. Based on different ranks of doctors toward AI; Medical Officers exhibited the highest proportion at (32.7%) of knowledge and understanding of AI concepts, followed by House Officers at (16.7%) (p=0.076); regarding attitude, Medical Officers demonstrated the highest (31.6%) favorable attitude, followed by House Officers at (17.5%) (p=0.229); In practice also, Medical Officer showed the highest portion (28.0%) among participants (p=0.129). Conclusion While there is a positive attitude and some level of AI practice, there remains a considerable gap in knowledge that needs addressing.
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Affiliation(s)
| | | | | | | | - Lina Hemmeda
- Faculty of Medicine, University of Khartoum, Khartoum
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Balas M, Mandelcorn ED, Yan P, Ing EB, Crawford SA, Arjmand P. ChatGPT and retinal disease: a cross-sectional study on AI comprehension of clinical guidelines. CANADIAN JOURNAL OF OPHTHALMOLOGY 2024:S0008-4182(24)00175-3. [PMID: 39097289 DOI: 10.1016/j.jcjo.2024.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 02/11/2024] [Accepted: 06/03/2024] [Indexed: 08/05/2024]
Abstract
OBJECTIVE To evaluate the performance of an artificial intelligence (AI) large language model, ChatGPT (version 4.0), for common retinal diseases, in accordance with the American Academy of Ophthalmology (AAO) Preferred Practice Pattern (PPP) guidelines. DESIGN A cross-sectional survey study design was employed to compare the responses made by ChatGPT to established clinical guidelines. PARTICIPANTS Responses by the AI were reviewed by a panel of three vitreoretinal specialists for evaluation. METHODS To investigate ChatGPT's comprehension of clinical guidelines, we designed 130 questions covering a broad spectrum of topics within 12 AAO PPP domains of retinal disease These questions were crafted to encompass diagnostic criteria, treatment guidelines, and management strategies, including both medical and surgical aspects of retinal care. A panel of 3 retinal specialists independently evaluated responses on a Likert scale from 1 to 5 based on their relevance, accuracy, and adherence to AAO PPP guidelines. Response readability was evaluated using Flesch Readability Ease and Flesch-Kincaid grade level scores. RESULTS ChatGPT achieved an overall average score of 4.9/5.0, suggesting high alignment with the AAO PPP guidelines. Scores varied across domains, with the lowest in the surgical management of disease. The responses had a low reading ease score and required a college-to-graduate level of comprehension. Identified errors were related to diagnostic criteria, treatment options, and methodological procedures. CONCLUSION ChatGPT 4.0 demonstrated significant potential in generating guideline-concordant responses, particularly for common medical retinal diseases. However, its performance slightly decreased in surgical retina, highlighting the ongoing need for clinician input, further model refinement, and improved comprehensibility.
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Affiliation(s)
- Michael Balas
- Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Efrem D Mandelcorn
- Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada; University Health Network, University of Toronto, Toronto, Ontario, Canada; Kensington Eye Institute, Toronto, Ontario, Canada
| | - Peng Yan
- Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada; University Health Network, University of Toronto, Toronto, Ontario, Canada; Kensington Eye Institute, Toronto, Ontario, Canada
| | - Edsel B Ing
- Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada; Department of Ophthalmology and Visual Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Sean A Crawford
- Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; University Health Network, University of Toronto, Toronto, Ontario, Canada; Division of Vascular Surgery, Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Parnian Arjmand
- Peter Munk Cardiac Centre, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada; Mississauga Retina Institute, Mississauga, Ontario, Canada.
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29
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Lin YT, Wang BC, Chung JY. Identifying Acute Aortic Syndrome and Thoracic Aortic Aneurysm from Chest Radiography in the Emergency Department Using Convolutional Neural Network Models. Diagnostics (Basel) 2024; 14:1646. [PMID: 39125522 PMCID: PMC11311574 DOI: 10.3390/diagnostics14151646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Revised: 07/28/2024] [Accepted: 07/28/2024] [Indexed: 08/12/2024] Open
Abstract
(1) Background: Identifying acute aortic syndrome (AAS) and thoracic aortic aneurysm (TAA) in busy emergency departments (EDs) is crucial due to their life-threatening nature, necessitating timely and accurate diagnosis. (2) Methods: This retrospective case-control study was conducted in the ED of three hospitals. Adult patients visiting the ED between 1 January 2010 and 1 January 2020 with a chief complaint of chest or back pain were enrolled in the study. The collected chest radiography (CXRs) data were divided into training (80%) and testing (20%) datasets. The training dataset was trained by four different convolutional neural network (CNN) models. (3) Results: A total of 1625 patients were enrolled in this study. The InceptionV3 model achieved the highest F1 score of 0.76. (4) Conclusions: Analysis of CXRs using a CNN-based model provides a novel tool for clinicians to interpret ED patients with chest pain and suspected AAS and TAA. The integration of such imaging tools into ED could be considered in the future to enhance the diagnostic workflow for clinically fatal diseases.
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Affiliation(s)
- Yang-Tse Lin
- Department of Emergency Medicine, Cathay General Hospital, Hsinchu Branch, Hsinchu 300003, Taiwan;
| | - Bing-Cheng Wang
- Department of Emergency Medicine, Sijhih Cathay General Hospital, New Taipei City 221037, Taiwan
| | - Jui-Yuan Chung
- Department of Emergency Medicine, Cathay General Hospital, Taipei City 106438, Taiwan
- School of Medicine, National Tsing Hua University, Hsinchu 300044, Taiwan
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Castner N, Arsiwala-Scheppach L, Mertens S, Krois J, Thaqi E, Kasneci E, Wahl S, Schwendicke F. Expert gaze as a usability indicator of medical AI decision support systems: a preliminary study. NPJ Digit Med 2024; 7:199. [PMID: 39068241 PMCID: PMC11283514 DOI: 10.1038/s41746-024-01192-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 07/12/2024] [Indexed: 07/30/2024] Open
Abstract
Given the current state of medical artificial intelligence (AI) and perceptions towards it, collaborative systems are becoming the preferred choice for clinical workflows. This work aims to address expert interaction with medical AI support systems to gain insight towards how these systems can be better designed with the user in mind. As eye tracking metrics have been shown to be robust indicators of usability, we employ them for evaluating the usability and user interaction with medical AI support systems. We use expert gaze to assess experts' interaction with an AI software for caries detection in bitewing x-ray images. We compared standard viewing of bitewing images without AI support versus viewing where AI support could be freely toggled on and off. We found that experts turned the AI on for roughly 25% of the total inspection task, and generally turned it on halfway through the course of the inspection. Gaze behavior showed that when supported by AI, more attention was dedicated to user interface elements related to the AI support, with more frequent transitions from the image itself to these elements. When considering that expert visual strategy is already optimized for fast and effective image inspection, such interruptions in attention can lead to increased time needed for the overall assessment. Gaze analysis provided valuable insights into an AI's usability for medical image inspection. Further analyses of these tools and how to delineate metrical measures of usability should be developed.
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Affiliation(s)
- Nora Castner
- Carl Zeiss Vision International GmbH, Tübingen, Germany.
- University of Tübingen, Tübingen, Germany.
| | | | - Sarah Mertens
- Charité - Univesitätsmedizin, Oral Diagnostics, Digital Health and Services Research, Berlin, Germany
| | - Joachim Krois
- Charité - Univesitätsmedizin, Oral Diagnostics, Digital Health and Services Research, Berlin, Germany
| | - Enkeleda Thaqi
- Technical University of Munich, Human-Centered Technologies for Learning, Munich, Germany
| | - Enkelejda Kasneci
- Technical University of Munich, Human-Centered Technologies for Learning, Munich, Germany
| | - Siegfried Wahl
- Carl Zeiss Vision International GmbH, Tübingen, Germany
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
| | - Falk Schwendicke
- Ludwig Maximilian University, Operative, Preventative and Pediatric Dentistry and Periodontology, Munich, Germany
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31
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Ye Z, Zhang D, Zhao Y, Chen M, Wang H, Seery S, Qu Y, Xue P, Jiang Y. Deep learning algorithms for melanoma detection using dermoscopic images: A systematic review and meta-analysis. Artif Intell Med 2024; 155:102934. [PMID: 39088883 DOI: 10.1016/j.artmed.2024.102934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 06/21/2024] [Accepted: 07/22/2024] [Indexed: 08/03/2024]
Abstract
BACKGROUND Melanoma is a serious risk to human health and early identification is vital for treatment success. Deep learning (DL) has the potential to detect cancer using imaging technologies and many studies provide evidence that DL algorithms can achieve high accuracy in melanoma diagnostics. OBJECTIVES To critically assess different DL performances in diagnosing melanoma using dermatoscopic images and discuss the relationship between dermatologists and DL. METHODS Ovid-Medline, Embase, IEEE Xplore, and the Cochrane Library were systematically searched from inception until 7th December 2021. Studies that reported diagnostic DL model performances in detecting melanoma using dermatoscopic images were included if they had specific outcomes and histopathologic confirmation. Binary diagnostic accuracy data and contingency tables were extracted to analyze outcomes of interest, which included sensitivity (SEN), specificity (SPE), and area under the curve (AUC). Subgroup analyses were performed according to human-machine comparison and cooperation. The study was registered in PROSPERO, CRD42022367824. RESULTS 2309 records were initially retrieved, of which 37 studies met our inclusion criteria, and 27 provided sufficient data for meta-analytical synthesis. The pooled SEN was 82 % (range 77-86), SPE was 87 % (range 84-90), with an AUC of 0.92 (range 0.89-0.94). Human-machine comparison had pooled AUCs of 0.87 (0.84-0.90) and 0.83 (0.79-0.86) for DL and dermatologists, respectively. Pooled AUCs were 0.90 (0.87-0.93), 0.80 (0.76-0.83), and 0.88 (0.85-0.91) for DL, and junior and senior dermatologists, respectively. Analyses of human-machine cooperation were 0.88 (0.85-0.91) for DL, 0.76 (0.72-0.79) for unassisted, and 0.87 (0.84-0.90) for DL-assisted dermatologists. CONCLUSIONS Evidence suggests that DL algorithms are as accurate as senior dermatologists in melanoma diagnostics. Therefore, DL could be used to support dermatologists in diagnostic decision-making. Although, further high-quality, large-scale multicenter studies are required to address the specific challenges associated with medical AI-based diagnostics.
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Affiliation(s)
- Zichen Ye
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Daqian Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuankai Zhao
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mingyang Chen
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Huike Wang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Samuel Seery
- Population Health Sciences Institute, School of Pharmacy, Newcastle University, Newcastle NE1 7RU, United Kingdom of Great Britain and Northern Ireland
| | - Yimin Qu
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Peng Xue
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Yu Jiang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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Hones K, Krisanda E, Chim H. Caution Regarding ChatGPT's Appropriateness and Reliability Regarding Surgery for Wrist Arthritis. Hand (N Y) 2024:15589447241265519. [PMID: 39045653 DOI: 10.1177/15589447241265519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/25/2024]
Abstract
BACKGROUND Chat Generative Pre-Trained Transformer (ChatGPT), an artificial intelligence (AI) program, is widely used for information compilation. This study sought to analyze the quality and consistency of the information generated by ChatGPT regarding common procedures for wrist arthritis. METHODS 32 standardized questions regarding wrist osteoarthritis and related procedures (4-corner-fusion [4CF], proximal row carpectomy [PRC], resurfacing capitate pyrocarbon implant, wrist denervation, and total wrist arthrodesis and arthroplasty) were presented to the ChatGPT-3.5 interface 3 separate times, without feedback. ChatGPT's answers were evaluated for medical accuracy by 3 reviewers and rated as "appropriate," "appropriate but incomplete," or "inappropriate." Ratings were then converted to numerical values to calculate an intraclass correlation coefficient (ICC). A DISCERN score was used to assess quality, and Flesch-Kincade Grade Level and Flesch Reading Ease Score for readability. RESULTS 75% of the responses were deemed "appropriate," with 23 questions receiving unanimous appropriate ratings across all responses. The ICC was 0.97 (95% CI [0.46, 0.98]), indicating excellent reliability. DISCERN score was 60 (good). The Flesch-Kincaid Grade Level was 14.6 ± 1.9, and the Flesch Reading Ease Score was 25.3 ± 6.7, implying a college reading level. The information that ChatGPT provided for PRC and total wrist arthrodesis and arthroplasty, appeared to be more reliable than for 4CF and denervation. CONCLUSION ChatGPT's reliability and accuracy of information varied across procedures, possibly due to unknown and diverse sources. Furthermore, while some answers were factually correct, many provided generic information across differing questions, limiting usefulness. ChatGPT must be used cautiously and the limitations understood.
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Viet CT, Zhang M, Dharmaraj N, Li GY, Pearson AT, Manon VA, Grandhi A, Xu K, Aouizerat BE, Young S. Artificial Intelligence Applications in Oral Cancer and Oral Dysplasia. Tissue Eng Part A 2024. [PMID: 39041628 DOI: 10.1089/ten.tea.2024.0096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2024] Open
Abstract
Oral squamous cell carcinoma (OSCC) is a highly unpredictable disease with devastating mortality rates that have not changed over the past decades, in the face of advancements in treatments and biomarkers, which have improved survival for other cancers. Delays in diagnosis are frequent, leading to more disfiguring treatments and poor outcomes in patients. The clinical challenge lies in identifying those patients at highest risk for developing OSCC. Oral epithelial dysplasia (OED) is a precursor of OSCC with highly variable behavior across patients. There is no reliable clinical, pathologic, histologic or molecular biomarker to determine individual risk in OED patients. Similarly, there are no robust biomarkers to predict treatment outcomes or mortality of OSCC patients. This review aims to highlight advancements in artificial intelligence (AI)-based methods to develop predictive biomarkers of OED transformation to OSCC or predictive biomarkers of OSCC mortality and treatment response. Machine-learning based biomarkers, such as S100A7, demonstrate promising appraisal for the risk of malignant transformation of OED. Machine learning-enhanced multiplex immunohistochemistry (mIHC) workflows examine immune cell patterns and organization within the tumor immune microenvironment to generate outcome predictions in immunotherapy. Deep learning (DL) is an AI-based method using an extended neural network or related architecture with multiple "hidden" layers of simulated neurons to combine simple visual features into complex patterns. DL-based digital pathology is currently being developed to assess OED and OSCC outcomes. The integration of machine learning in epigenomics aims to examine the epigenetic modification of diseases and improve our ability to detect, classify, and predict outcomes associated with epigenetic marks. Collectively, these tools showcase promising advancements in discovery and technology, which may provide a potential solution to addressing the current limitations in predicting OED transformation and OSCC behavior, both of which are clinical challenges that must be addressed in order to improve OSCC survival.
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Affiliation(s)
- Chi Tonglien Viet
- Loma Linda University, Department of Oral and Maxillofacial Surgery, Loma Linda, California, United States;
| | - Michael Zhang
- Loma Linda University, Department of Oral and Maxillofacial Surgery, Loma Linda, California, United States;
| | - Neeraja Dharmaraj
- The University of Texas Health Science Center at Houston School of Dentistry, Bernard & Gloria Pepper Katz Department of Oral and Maxillofacial Surgery, Houston, Texas, United States;
| | - Grace Y Li
- The University of Chicago Medical Center, Department of Medicine, Section of Hematology/Oncology,, Chicago, Illinois, United States;
| | - Alexander T Pearson
- The University of Chicago Medical Center, Department of Medicine, Section of Hematology/Oncology,, Chicago, Illinois, United States;
| | - Victoria A Manon
- The University of Texas Health Science Center at Houston School of Dentistry, Bernard & Gloria Pepper Katz Department of Oral and Maxillofacial Surgery, Houston, Texas, United States;
| | - Anupama Grandhi
- Loma Linda University, Department of Oral and Maxillofacial Surgery, Loma Linda, California, United States;
| | - Ke Xu
- Yale School of Medicine, Department of Psychiatry, New Haven, Connecticut, United States
- VA Connecticut Healthcare System - West Haven Campus, West Haven, Connecticut, United States;
| | - Bradley E Aouizerat
- New York University College of Dentistry, Translational Research Center, New York, New York, United States;
| | - Simon Young
- The University of Texas Health Science Center at Houston School of Dentistry, Bernard & Gloria Pepper Katz Department of Oral and Maxillofacial Surgery, Houston, Texas, United States;
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Campagner A, Milella F, Banfi G, Cabitza F. Second opinion machine learning for fast-track pathway assignment in hip and knee replacement surgery: the use of patient-reported outcome measures. BMC Med Inform Decis Mak 2024; 24:203. [PMID: 39044277 PMCID: PMC11267678 DOI: 10.1186/s12911-024-02602-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 07/09/2024] [Indexed: 07/25/2024] Open
Abstract
BACKGROUND The frequency of hip and knee arthroplasty surgeries has been rising steadily in recent decades. This trend is attributed to an aging population, leading to increased demands on healthcare systems. Fast Track (FT) surgical protocols, perioperative procedures designed to expedite patient recovery and early mobilization, have demonstrated efficacy in reducing hospital stays, convalescence periods, and associated costs. However, the criteria for selecting patients for FT procedures have not fully capitalized on the available patient data, including patient-reported outcome measures (PROMs). METHODS Our study focused on developing machine learning (ML) models to support decision making in assigning patients to FT procedures, utilizing data from patients' self-reported health status. These models are specifically designed to predict the potential health status improvement in patients initially selected for FT. Our approach focused on techniques inspired by the concept of controllable AI. This includes eXplainable AI (XAI), which aims to make the model's recommendations comprehensible to clinicians, and cautious prediction, a method used to alert clinicians about potential control losses, thereby enhancing the models' trustworthiness and reliability. RESULTS Our models were trained and tested using a dataset comprising 899 records from individual patients admitted to the FT program at IRCCS Ospedale Galeazzi-Sant'Ambrogio. After training and selecting hyper-parameters, the models were assessed using a separate internal test set. The interpretable models demonstrated performance on par or even better than the most effective 'black-box' model (Random Forest). These models achieved sensitivity, specificity, and positive predictive value (PPV) exceeding 70%, with an area under the curve (AUC) greater than 80%. The cautious prediction models exhibited enhanced performance while maintaining satisfactory coverage (over 50%). Further, when externally validated on a separate cohort from the same hospital-comprising patients from a subsequent time period-the models showed no pragmatically notable decline in performance. CONCLUSIONS Our results demonstrate the effectiveness of utilizing PROMs as basis to develop ML models for planning assignments to FT procedures. Notably, the application of controllable AI techniques, particularly those based on XAI and cautious prediction, emerges as a promising approach. These techniques provide reliable and interpretable support, essential for informed decision-making in clinical processes.
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Affiliation(s)
| | - Frida Milella
- Department of Computer Science, Systems and Communication, University of Milano-Bicocca, Milan, Italy
| | - Giuseppe Banfi
- IRCCS Ospedale Galeazzi Sant'Ambrogio, Milan, Italy
- Faculty of Medicine and Surgery, Universitá Vita-Salute San Raffaele, Milan, Italy
| | - Federico Cabitza
- IRCCS Ospedale Galeazzi Sant'Ambrogio, Milan, Italy
- Department of Computer Science, Systems and Communication, University of Milano-Bicocca, Milan, Italy
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Fu DS, Adili A, Chen X, Li JZ, Muheremu A. Abnormal genes and pathways that drive muscle contracture from brachial plexus injuries: Towards machine learning approach. SLAS Technol 2024:100166. [PMID: 39033877 DOI: 10.1016/j.slast.2024.100166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 06/24/2024] [Accepted: 07/18/2024] [Indexed: 07/23/2024]
Abstract
In order to clarify the pathways closely linked to denervated muscle contracture, this work uses IoMT-enabled healthcare stratergies to examine changes in gene expression patterns inside atrophic muscles following brachial plexus damage. The gene expression Omnibus (GEO) database searching was used to locate the dataset GSE137606, which is connected to brachial plexus injuries. Strict criteria (|logFC|≥2 & adj.p < 0.05) were used to extract differentially expressed genes (DEGs). To identify dysregulated activities and pathways in denervated muscles, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were used. Hub genes were found using Cytoscape software's algorithms, which took into account parameters like as proximity, degree, and MNC. Their expression, enriched pathways, and correlations were then examined. The results showed that 316 DEGs were predominantly concentrated in muscle-related processes such as tissue formation and contraction pathways. Of these, 297 DEGs were highly expressed in denervated muscles, whereas 19 DEGs were weakly expressed. GSEA showed improvements in the contraction of striated and skeletal muscles. In addition, it was shown that in denervated muscles, Myod1, Myog, Myh7, Myl2, Tnnt2, and Tnni1 were elevated hub genes with enriched pathways such adrenergic signaling and tight junction. These results point to possible therapeutic targets for denervated muscular contracture, including Myod1, Myog, Myh7, Myl2, Tnnt2, and Tnni1. This highlights treatment options for this ailment which enhances the mental state of patient.
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Affiliation(s)
- Dong-Sheng Fu
- Department of Hand and foot microsurgery, The sixth affiliated hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830002, China
| | - Alimujiang Adili
- Department of Hand and foot microsurgery, The sixth affiliated hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830002, China
| | - Xuan Chen
- Department of Hand and foot microsurgery, The sixth affiliated hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830002, China
| | - Jian-Zhu Li
- Department of Hand and foot microsurgery, The sixth affiliated hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830002, China
| | - Aikeremu Muheremu
- Department of Hand and foot microsurgery, The sixth affiliated hospital of Xinjiang Medical University, Urumqi, Xinjiang, 830002, China.
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Hung Y, Lin C, Lin CS, Lee CC, Fang WH, Lee CC, Wang CH, Tsai DJ. Artificial Intelligence-Enabled Electrocardiography Predicts Future Pacemaker Implantation and Adverse Cardiovascular Events. J Med Syst 2024; 48:67. [PMID: 39028354 DOI: 10.1007/s10916-024-02088-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 07/11/2024] [Indexed: 07/20/2024]
Abstract
Medical advances prolonging life have led to more permanent pacemaker implants. When pacemaker implantation (PMI) is commonly caused by sick sinus syndrome or conduction disorders, predicting PMI is challenging, as patients often experience related symptoms. This study was designed to create a deep learning model (DLM) for predicting future PMI from ECG data and assess its ability to predict future cardiovascular events. In this study, a DLM was trained on a dataset of 158,471 ECGs from 42,903 academic medical center patients, with additional validation involving 25,640 medical center patients and 26,538 community hospital patients. Primary analysis focused on predicting PMI within 90 days, while all-cause mortality, cardiovascular disease (CVD) mortality, and the development of various cardiovascular conditions were addressed with secondary analysis. The study's raw ECG DLM achieved area under the curve (AUC) values of 0.870, 0.878, and 0.883 for PMI prediction within 30, 60, and 90 days, respectively, along with sensitivities exceeding 82.0% and specificities over 81.9% in the internal validation. Significant ECG features included the PR interval, corrected QT interval, heart rate, QRS duration, P-wave axis, T-wave axis, and QRS complex axis. The AI-predicted PMI group had higher risks of PMI after 90 days (hazard ratio [HR]: 7.49, 95% CI: 5.40-10.39), all-cause mortality (HR: 1.91, 95% CI: 1.74-2.10), CVD mortality (HR: 3.53, 95% CI: 2.73-4.57), and new-onset adverse cardiovascular events. External validation confirmed the model's accuracy. Through ECG analyses, our AI DLM can alert clinicians and patients to the possibility of future PMI and related mortality and cardiovascular risks, aiding in timely patient intervention.
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Affiliation(s)
- Yuan Hung
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center Taipei, Taipei, Taiwan, R.O.C
| | - Chin Lin
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
- School of Public Health, National Defense Medical Center, Taipei, Taiwan, R.O.C
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Chin-Sheng Lin
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center Taipei, Taipei, Taiwan, R.O.C
| | - Chiao-Chin Lee
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center Taipei, Taipei, Taiwan, R.O.C
| | - Wen-Hui Fang
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
- Department of Family and Community Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Chia-Cheng Lee
- Medical Informatics Office, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
- Division of Colorectal Surgery, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Chih-Hung Wang
- Department of Otolaryngology-Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan, R.O.C
| | - Dung-Jang Tsai
- Artificial Intelligence of Things Center, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, R.O.C..
- Medical Technology Education Center, School of Medicine, National Defense Medical Center, Taipei, Taiwan, R.O.C..
- Department of Statistics and Information Science, Fu Jen Catholic University, No. 510, Zhongzheng Rd., Xinzhuang Dist, New Taipei City, 242062, Taiwan, R.O.C..
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Hou L, Liang X, Zeng L, Wang Q, Chen Z. Conventional and modern markers of pregnancy of unknown location: Update and narrative review. Int J Gynaecol Obstet 2024. [PMID: 39022869 DOI: 10.1002/ijgo.15807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 07/08/2024] [Indexed: 07/20/2024]
Abstract
Pregnancy of unknown location (PUL) is a temporary pathologic or physiologic phenomenon of early pregnancy that requires follow up to determine the final pregnancy outcome. Evidence indicated that PUL patients suffer a remarkably higher rate of adverse pregnancy outcomes, represented by ectopic gestation and early pregnancy loss, than the general population. In the past few decades, discussion about PUL has never stopped, and a variety of markers have been widely investigated for the early and accurate evaluation of PUL, including serum biomarkers, ultrasound imaging features, multivariate analysis, and the diagnosis of ectopic pregnancy based on risk stratification. So far, machine learning (ML) methods represented by M4 and M6 logistic regression have gained a level of recognition and are continually improving. Nevertheless, the heterogeneity of PUL markers, mainly caused by the limited sample size, the differences in population and technical maturity, etc., have hampered the management of PUL. With the advancement of multidisciplinary integration and cutting-edge technologies (e.g. artificial intelligence, prediction model development, and telemedicine), novel markers, and strategies for the management of PUL are expected to be developed. In this review, we summarize both conventional and novel markers (represented by artificial intelligence) for PUL assessment and management, investigate their advancements, limitations and challenges, and propose insights on future research direction and clinical application.
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Affiliation(s)
- Likang Hou
- Institute of Medical Imaging, Hengyang Medical School, University of South China, Hengyang, China
- The First Affiliated Hospital, Medical Imaging Center, Hengyang Medical School, University of South China, Hengyang, China
| | - Xiaowen Liang
- Institute of Medical Imaging, Hengyang Medical School, University of South China, Hengyang, China
- Key Laboratory of Medical Imaging Precision Theranostics and Radiation Protection, College of Hunan Province, Department of Medical Imaging, the Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China
| | - Lingqing Zeng
- Institute of Medical Imaging, Hengyang Medical School, University of South China, Hengyang, China
- The First Affiliated Hospital, Medical Imaging Center, Hengyang Medical School, University of South China, Hengyang, China
| | - Qian Wang
- The First Affiliated Hospital, Center for Reproductive Medicine, Hengyang Medical School, University of South China, Hengyang, China
| | - Zhiyi Chen
- Institute of Medical Imaging, Hengyang Medical School, University of South China, Hengyang, China
- Key Laboratory of Medical Imaging Precision Theranostics and Radiation Protection, College of Hunan Province, Department of Medical Imaging, the Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, China
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Tolentino R, Baradaran A, Gore G, Pluye P, Abbasgholizadeh-Rahimi S. Curriculum Frameworks and Educational Programs in AI for Medical Students, Residents, and Practicing Physicians: Scoping Review. JMIR MEDICAL EDUCATION 2024; 10:e54793. [PMID: 39023999 PMCID: PMC11294785 DOI: 10.2196/54793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 03/26/2024] [Accepted: 04/29/2024] [Indexed: 07/20/2024]
Abstract
BACKGROUND The successful integration of artificial intelligence (AI) into clinical practice is contingent upon physicians' comprehension of AI principles and its applications. Therefore, it is essential for medical education curricula to incorporate AI topics and concepts, providing future physicians with the foundational knowledge and skills needed. However, there is a knowledge gap in the current understanding and availability of structured AI curriculum frameworks tailored for medical education, which serve as vital guides for instructing and facilitating the learning process. OBJECTIVE The overall aim of this study is to synthesize knowledge from the literature on curriculum frameworks and current educational programs that focus on the teaching and learning of AI for medical students, residents, and practicing physicians. METHODS We followed a validated framework and the Joanna Briggs Institute methodological guidance for scoping reviews. An information specialist performed a comprehensive search from 2000 to May 2023 in the following bibliographic databases: MEDLINE (Ovid), Embase (Ovid), CENTRAL (Cochrane Library), CINAHL (EBSCOhost), and Scopus as well as the gray literature. Papers were limited to English and French languages. This review included papers that describe curriculum frameworks for teaching and learning AI in medicine, irrespective of country. All types of papers and study designs were included, except conference abstracts and protocols. Two reviewers independently screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. We adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting the results. RESULTS Of the 5104 papers screened, 21 papers relevant to our eligibility criteria were identified. In total, 90% (19/21) of the papers altogether described 30 current or previously offered educational programs, and 10% (2/21) of the papers described elements of a curriculum framework. One framework describes a general approach to integrating AI curricula throughout the medical learning continuum and another describes a core curriculum for AI in ophthalmology. No papers described a theory, pedagogy, or framework that guided the educational programs. CONCLUSIONS This review synthesizes recent advancements in AI curriculum frameworks and educational programs within the domain of medical education. To build on this foundation, future researchers are encouraged to engage in a multidisciplinary approach to curriculum redesign. In addition, it is encouraged to initiate dialogues on the integration of AI into medical curriculum planning and to investigate the development, deployment, and appraisal of these innovative educational programs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.11124/JBIES-22-00374.
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Affiliation(s)
- Raymond Tolentino
- Department of Family Medicine, McGill University, Montreal, QC, Canada
| | - Ashkan Baradaran
- Department of Family Medicine, McGill University, Montreal, QC, Canada
| | - Genevieve Gore
- Schulich Library of Physical Sciences, Life Sciences, and Engineering, McGill University, Montreal, QC, Canada
| | - Pierre Pluye
- Department of Family Medicine, McGill University, Montreal, QC, Canada
| | - Samira Abbasgholizadeh-Rahimi
- Department of Family Medicine, McGill University, Montreal, QC, Canada
- Mila - Quebec AI Institute, Montreal, QC, Canada
- Lady Davis Institute for Medical Research, Herzl Family Practice Centre, Jewish General Hospital, Montreal, QC, Canada
- Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, QC, Canada
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Weir VR, Dempsey K, Gichoya JW, Rotemberg V, Wong AKI. A survey of skin tone assessment in prospective research. NPJ Digit Med 2024; 7:191. [PMID: 39014060 PMCID: PMC11252344 DOI: 10.1038/s41746-024-01176-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Accepted: 06/21/2024] [Indexed: 07/18/2024] Open
Abstract
Increasing evidence supports reduced accuracy of noninvasive assessment tools, such as pulse oximetry, temperature probes, and AI skin diagnosis benchmarks, in patients with darker skin tones. The FDA is exploring potential strategies for device regulation to improve performance across diverse skin tones by including skin tone criteria. However, there is no consensus about how prospective studies should perform skin tone assessment in order to take this bias into account. There are several tools available to conduct skin tone assessments including administered visual scales (e.g., Fitzpatrick Skin Type, Pantone, Monk Skin Tone) and color measurement tools (e.g., reflectance colorimeters, reflectance spectrophotometers, cameras), although none are consistently used or validated across multiple medical domains. Accurate and consistent skin tone measurement depends on many factors including standardized environments, lighting, body parts assessed, patient conditions, and choice of skin tone assessment tool(s). As race and ethnicity are inadequate proxies for skin tone, these considerations can be helpful in standardizing the effect of skin tone on studies such as AI dermatology diagnoses, pulse oximetry, and temporal thermometers. Skin tone bias in medical devices is likely due to systemic factors that lead to inadequate validation across diverse skin tones. There is an opportunity for researchers to use skin tone assessment methods with standardized considerations in prospective studies of noninvasive tools that may be affected by skin tone. We propose considerations that researchers must take in order to improve device robustness to skin tone bias.
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Affiliation(s)
- Vanessa R Weir
- Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Katelyn Dempsey
- Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Duke University, Durham, NC, USA
| | - Judy Wawira Gichoya
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Veronica Rotemberg
- Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - An-Kwok Ian Wong
- Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Duke University, Durham, NC, USA.
- Department of Biostatistics and Bioinformatics, Division of Translational Biomedical Informatics, Duke University, Durham, NC, USA.
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Ganesan P, Feng R, Deb B, Tjong FVY, Rogers AJ, Ruipérez-Campillo S, Somani S, Clopton P, Baykaner T, Rodrigo M, Zou J, Haddad F, Zaharia M, Narayan SM. Novel Domain Knowledge-Encoding Algorithm Enables Label-Efficient Deep Learning for Cardiac CT Segmentation to Guide Atrial Fibrillation Treatment in a Pilot Dataset. Diagnostics (Basel) 2024; 14:1538. [PMID: 39061675 PMCID: PMC11276420 DOI: 10.3390/diagnostics14141538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 07/07/2024] [Accepted: 07/10/2024] [Indexed: 07/28/2024] Open
Abstract
Background: Segmenting computed tomography (CT) is crucial in various clinical applications, such as tailoring personalized cardiac ablation for managing cardiac arrhythmias. Automating segmentation through machine learning (ML) is hindered by the necessity for large, labeled training data, which can be challenging to obtain. This article proposes a novel approach for automated, robust labeling using domain knowledge to achieve high-performance segmentation by ML from a small training set. The approach, the domain knowledge-encoding (DOKEN) algorithm, reduces the reliance on large training datasets by encoding cardiac geometry while automatically labeling the training set. The method was validated in a hold-out dataset of CT results from an atrial fibrillation (AF) ablation study. Methods: The DOKEN algorithm parses left atrial (LA) structures, extracts "anatomical knowledge" by leveraging digital LA models (available publicly), and then applies this knowledge to achieve high ML segmentation performance with a small number of training samples. The DOKEN-labeled training set was used to train a nnU-Net deep neural network (DNN) model for segmenting cardiac CT in N = 20 patients. Subsequently, the method was tested in a hold-out set with N = 100 patients (five times larger than training set) who underwent AF ablation. Results: The DOKEN algorithm integrated with the nn-Unet model achieved high segmentation performance with few training samples, with a training to test ratio of 1:5. The Dice score of the DOKEN-enhanced model was 96.7% (IQR: 95.3% to 97.7%), with a median error in surface distance of boundaries of 1.51 mm (IQR: 0.72 to 3.12) and a mean centroid-boundary distance of 1.16 mm (95% CI: -4.57 to 6.89), similar to expert results (r = 0.99; p < 0.001). In digital hearts, the novel DOKEN approach segmented the LA structures with a mean difference for the centroid-boundary distances of -0.27 mm (95% CI: -3.87 to 3.33; r = 0.99; p < 0.0001). Conclusions: The proposed novel domain knowledge-encoding algorithm was able to perform the segmentation of six substructures of the LA, reducing the need for large training data sets. The combination of domain knowledge encoding and a machine learning approach could reduce the dependence of ML on large training datasets and could potentially be applied to AF ablation procedures and extended in the future to other imaging, 3D printing, and data science applications.
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Affiliation(s)
- Prasanth Ganesan
- Department of Medicine and Stanford Cardiovascular Institute (CVI), Stanford University, Stanford, CA 94305, USA; (P.G.); (R.F.)
| | - Ruibin Feng
- Department of Medicine and Stanford Cardiovascular Institute (CVI), Stanford University, Stanford, CA 94305, USA; (P.G.); (R.F.)
| | - Brototo Deb
- Department of Medicine and Stanford Cardiovascular Institute (CVI), Stanford University, Stanford, CA 94305, USA; (P.G.); (R.F.)
| | - Fleur V. Y. Tjong
- Department of Medicine and Stanford Cardiovascular Institute (CVI), Stanford University, Stanford, CA 94305, USA; (P.G.); (R.F.)
- Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands
| | - Albert J. Rogers
- Department of Medicine and Stanford Cardiovascular Institute (CVI), Stanford University, Stanford, CA 94305, USA; (P.G.); (R.F.)
| | - Samuel Ruipérez-Campillo
- Department of Medicine and Stanford Cardiovascular Institute (CVI), Stanford University, Stanford, CA 94305, USA; (P.G.); (R.F.)
- Department of Computer Science, ETH Zurich, 8092 Zurich, Switzerland
| | - Sulaiman Somani
- Department of Medicine and Stanford Cardiovascular Institute (CVI), Stanford University, Stanford, CA 94305, USA; (P.G.); (R.F.)
| | - Paul Clopton
- Department of Medicine and Stanford Cardiovascular Institute (CVI), Stanford University, Stanford, CA 94305, USA; (P.G.); (R.F.)
| | - Tina Baykaner
- Department of Medicine and Stanford Cardiovascular Institute (CVI), Stanford University, Stanford, CA 94305, USA; (P.G.); (R.F.)
| | - Miguel Rodrigo
- Department of Medicine and Stanford Cardiovascular Institute (CVI), Stanford University, Stanford, CA 94305, USA; (P.G.); (R.F.)
- CoMMLab, Universitat de València, 46100 Valencia, Spain
| | - James Zou
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Francois Haddad
- Department of Medicine and Stanford Cardiovascular Institute (CVI), Stanford University, Stanford, CA 94305, USA; (P.G.); (R.F.)
| | - Matei Zaharia
- Department of Computer Science, University of California Berkeley, Berkeley, CA 94720, USA
| | - Sanjiv M. Narayan
- Department of Medicine and Stanford Cardiovascular Institute (CVI), Stanford University, Stanford, CA 94305, USA; (P.G.); (R.F.)
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Campanioni S, Veiga C, Prieto-González JM, González-Nóvoa JA, Busto L, Martinez C, Alberte-Woodward M, García de Soto J, Pouso-Diz J, Fernández Ceballos MDLÁ, Agis-Balboa RC. Explainable machine learning on baseline MRI predicts multiple sclerosis trajectory descriptors. PLoS One 2024; 19:e0306999. [PMID: 39012871 PMCID: PMC11251627 DOI: 10.1371/journal.pone.0306999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 06/26/2024] [Indexed: 07/18/2024] Open
Abstract
Multiple sclerosis (MS) is a multifaceted neurological condition characterized by challenges in timely diagnosis and personalized patient management. The application of Artificial Intelligence (AI) to MS holds promises for early detection, accurate diagnosis, and predictive modeling. The objectives of this study are: 1) to propose new MS trajectory descriptors that could be employed in Machine Learning (ML) regressors and classifiers to predict patient evolution; 2) to explore the contribution of ML models in discerning MS trajectory descriptors using only baseline Magnetic Resonance Imaging (MRI) studies. This study involved 446 MS patients who had a baseline MRI, at least two measurements of Expanded Disability Status Scale (EDSS), and a 1-year follow-up. Patients were divided into two groups: 1) for model development and 2) for evaluation. Three descriptors: β1, β2, and EDSS(t), were related to baseline MRI parameters using regression and classification XGBoost models. Shapley Additive Explanations (SHAP) analysis enhanced model transparency by identifying influential features. The results of this study demonstrate the potential of AI in predicting MS progression using the proposed patient trajectories and baseline MRI scans, outperforming classic Multiple Linear Regression (MLR) methods. In conclusion, MS trajectory descriptors are crucial; incorporating AI analysis into MRI assessments presents promising opportunities to advance predictive capabilities. SHAP analysis enhances model interpretation, revealing feature importance for clinical decisions.
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Affiliation(s)
- Silvia Campanioni
- Galicia Sur Health Research Institute (IIS Galicia Sur), Cardiovascular Research Group, Vigo, Spain
| | - César Veiga
- Galicia Sur Health Research Institute (IIS Galicia Sur), Cardiovascular Research Group, Vigo, Spain
| | - José María Prieto-González
- Health Research Institute of Santiago de Compostela (IDIS), Translational Research in Neurological Diseases Group, Santiago University Hospital Complex, SERGAS-USC, Santiago de Compostela, Spain
- Neuro Epigenetics Lab, Health Research Institute of Santiago de Compostela (IDIS), Santiago University Hospital Complex, Santiago de Compostela, Spain
- Neurology Service, Santiago University Hospital Complex, Santiago de Compostela, Spain
| | - José A. González-Nóvoa
- Galicia Sur Health Research Institute (IIS Galicia Sur), Cardiovascular Research Group, Vigo, Spain
| | - Laura Busto
- Galicia Sur Health Research Institute (IIS Galicia Sur), Cardiovascular Research Group, Vigo, Spain
| | - Carlos Martinez
- Galicia Sur Health Research Institute (IIS Galicia Sur), Cardiovascular Research Group, Vigo, Spain
| | - Miguel Alberte-Woodward
- Health Research Institute of Santiago de Compostela (IDIS), Translational Research in Neurological Diseases Group, Santiago University Hospital Complex, SERGAS-USC, Santiago de Compostela, Spain
- Neuro Epigenetics Lab, Health Research Institute of Santiago de Compostela (IDIS), Santiago University Hospital Complex, Santiago de Compostela, Spain
- Neurology Service, Santiago University Hospital Complex, Santiago de Compostela, Spain
| | - Jesús García de Soto
- Health Research Institute of Santiago de Compostela (IDIS), Translational Research in Neurological Diseases Group, Santiago University Hospital Complex, SERGAS-USC, Santiago de Compostela, Spain
- Neuro Epigenetics Lab, Health Research Institute of Santiago de Compostela (IDIS), Santiago University Hospital Complex, Santiago de Compostela, Spain
- Neurology Service, Santiago University Hospital Complex, Santiago de Compostela, Spain
| | - Jessica Pouso-Diz
- Health Research Institute of Santiago de Compostela (IDIS), Translational Research in Neurological Diseases Group, Santiago University Hospital Complex, SERGAS-USC, Santiago de Compostela, Spain
- Neuro Epigenetics Lab, Health Research Institute of Santiago de Compostela (IDIS), Santiago University Hospital Complex, Santiago de Compostela, Spain
- Neurology Service, Santiago University Hospital Complex, Santiago de Compostela, Spain
| | - María de los Ángeles Fernández Ceballos
- Health Research Institute of Santiago de Compostela (IDIS), Translational Research in Neurological Diseases Group, Santiago University Hospital Complex, SERGAS-USC, Santiago de Compostela, Spain
- Neuro Epigenetics Lab, Health Research Institute of Santiago de Compostela (IDIS), Santiago University Hospital Complex, Santiago de Compostela, Spain
- Neurology Service, Santiago University Hospital Complex, Santiago de Compostela, Spain
| | - Roberto Carlos Agis-Balboa
- Health Research Institute of Santiago de Compostela (IDIS), Translational Research in Neurological Diseases Group, Santiago University Hospital Complex, SERGAS-USC, Santiago de Compostela, Spain
- Neuro Epigenetics Lab, Health Research Institute of Santiago de Compostela (IDIS), Santiago University Hospital Complex, Santiago de Compostela, Spain
- Neurology Service, Santiago University Hospital Complex, Santiago de Compostela, Spain
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Aggarwal N, Drew DA, Parikh RB, Guha S. Ethical Implications of Artificial Intelligence in Gastroenterology: The Co-pilot or the Captain? Dig Dis Sci 2024:10.1007/s10620-024-08557-9. [PMID: 39009918 DOI: 10.1007/s10620-024-08557-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Accepted: 06/25/2024] [Indexed: 07/17/2024]
Abstract
Though artificial intelligence (AI) is being widely implemented in gastroenterology (GI) and hepatology and has the potential to be paradigm shifting for clinical practice, its pitfalls must be considered along with its advantages. Currently, although the use of AI is limited in practice to supporting clinical judgment, medicine is rapidly heading toward a global environment where AI will be increasingly autonomous. Broader implementation of AI will require careful ethical considerations, specifically related to bias, privacy, and consent. Widespread use of AI raises concerns related to increasing rates of systematic errors, potentially due to bias introduced in training datasets. We propose that a central repository for collection and analysis for training and validation datasets is essential to overcoming potential biases. Since AI does not have built-in concepts of bias and equality, humans involved in AI development and implementation must ensure its ethical use and development. Moreover, ethical concerns regarding data ownership and health information privacy are likely to emerge, obviating traditional methods of obtaining patient consent that cover all possible uses of patient data. The question of liability in case of adverse events related to use of AI in GI must be addressed among the physician, the healthcare institution, and the AI developer. Though the future of AI in GI is very promising, herein we review the ethical considerations in need of additional guidance informed by community experience and collective expertise.
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Affiliation(s)
- Nishant Aggarwal
- Department of Internal Medicine, William Beaumont University Hospital, Royal Oak, MI, USA
| | - David A Drew
- Clinical & Translational Epidemiology Unit, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ravi B Parikh
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Sushovan Guha
- Gastroenterology and Hepatology, Houston Regional Gastroenterology Institute (HRGI), Houston, TX, USA.
- Department of Clinical Sciences, Tilman J. Fertitta Family College of Medicine, University of Houston, Houston, TX, USA.
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Chen MY, Cao MQ, Xu TY. Progress in the application of artificial intelligence in skin wound assessment and prediction of healing time. Am J Transl Res 2024; 16:2765-2776. [PMID: 39114681 PMCID: PMC11301465 DOI: 10.62347/myhe3488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 05/22/2024] [Indexed: 08/10/2024]
Abstract
Since the 1970s, artificial intelligence (AI) has played an increasingly pivotal role in the medical field, enhancing the efficiency of disease diagnosis and treatment. Amidst an aging population and the proliferation of chronic disease, the prevalence of complex surgeries for high-risk multimorbid patients and hard-to-heal wounds has escalated. Healthcare professionals face the challenge of delivering safe and effective care to all patients concurrently. Inadequate management of skin wounds exacerbates the risk of infection and complications, which can obstruct the healing process and diminish patients' quality of life. AI shows substantial promise in revolutionizing wound care and management, thus enhancing the treatment of hospitalized patients and enabling healthcare workers to allocate their time more effectively. This review details the advancements in applying AI for skin wound assessment and the prediction of healing timelines. It emphasizes the use of diverse algorithms to automate and streamline the measurement, classification, and identification of chronic wound healing stages, and to predict wound healing times. Moreover, the review addresses existing limitations and explores future directions.
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Affiliation(s)
- Ming-Yao Chen
- Department of Anesthetic Pharmacology, School of Anesthesiology, Second Military Medical University/Naval Medical UniversityShanghai 200433, China
| | - Ming-Qi Cao
- Department of Anesthetic Pharmacology, School of Anesthesiology, Second Military Medical University/Naval Medical UniversityShanghai 200433, China
- College of Basic Medicine, Second Military Medical University/Naval Medical UniversityShanghai 200433, China
| | - Tian-Ying Xu
- Department of Anesthetic Pharmacology, School of Anesthesiology, Second Military Medical University/Naval Medical UniversityShanghai 200433, China
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Abi-Rafeh J, Henry N, Xu HH, Bassiri-Tehrani B, Arezki A, Kazan R, Gilardino MS, Nahai F. Utility and Comparative Performance of Current Artificial Intelligence Large Language Models as Postoperative Medical Support Chatbots in Aesthetic Surgery. Aesthet Surg J 2024; 44:889-896. [PMID: 38318684 DOI: 10.1093/asj/sjae025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 01/24/2024] [Accepted: 01/26/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND Large language models (LLMs) have revolutionized the way plastic surgeons and their patients can access and leverage artificial intelligence (AI). OBJECTIVES The present study aims to compare the performance of 2 current publicly available and patient-accessible LLMs in the potential application of AI as postoperative medical support chatbots in an aesthetic surgeon's practice. METHODS Twenty-two simulated postoperative patient presentations following aesthetic breast plastic surgery were devised and expert-validated. Complications varied in their latency within the postoperative period, as well as urgency of required medical attention. In response to each patient-reported presentation, Open AI's ChatGPT and Google's Bard, in their unmodified and freely available versions, were objectively assessed for their comparative accuracy in generating an appropriate differential diagnosis, most-likely diagnosis, suggested medical disposition, treatments or interventions to begin from home, and/or red flag signs/symptoms indicating deterioration. RESULTS ChatGPT cumulatively and significantly outperformed Bard across all objective assessment metrics examined (66% vs 55%, respectively; P < .05). Accuracy in generating an appropriate differential diagnosis was 61% for ChatGPT vs 57% for Bard (P = .45). ChatGPT asked an average of 9.2 questions on history vs Bard's 6.8 questions (P < .001), with accuracies of 91% vs 68% reporting the most-likely diagnosis, respectively (P < .01). Appropriate medical dispositions were suggested with accuracies of 50% by ChatGPT vs 41% by Bard (P = .40); appropriate home interventions/treatments with accuracies of 59% vs 55% (P = .94), and red flag signs/symptoms with accuracies of 79% vs 54% (P < .01), respectively. Detailed and comparative performance breakdowns according to complication latency and urgency are presented. CONCLUSIONS ChatGPT represents the superior LLM for the potential application of AI technology in postoperative medical support chatbots. Imperfect performance and limitations discussed may guide the necessary refinement to facilitate adoption.
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Cabanillas Silva P, Sun H, Rodriguez P, Rezk M, Zhang X, Fliegenschmidt J, Hulde N, von Dossow V, Meesseman L, Depraetere K, Szymanowsky R, Stieg J, Dahlweid FM. Evaluating gender bias in ML-based clinical risk prediction models: A study on multiple use cases at different hospitals. J Biomed Inform 2024; 157:104692. [PMID: 39009174 DOI: 10.1016/j.jbi.2024.104692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 07/01/2024] [Accepted: 07/12/2024] [Indexed: 07/17/2024]
Abstract
BACKGROUND An inherent difference exists between male and female bodies, the historical under-representation of females in clinical trials widened this gap in existing healthcare data. The fairness of clinical decision-support tools is at risk when developed based on biased data. This paper aims to quantitatively assess the gender bias in risk prediction models. We aim to generalize our findings by performing this investigation on multiple use cases at different hospitals. METHODS First, we conduct a thorough analysis of the source data to find gender-based disparities. Secondly, we assess the model performance on different gender groups at different hospitals and on different use cases. Performance evaluation is quantified using the area under the receiver-operating characteristic curve (AUROC). Lastly, we investigate the clinical implications of these biases by analyzing the underdiagnosis and overdiagnosis rate, and the decision curve analysis (DCA). We also investigate the influence of model calibration on mitigating gender-related disparities in decision-making processes. RESULTS Our data analysis reveals notable variations in incidence rates, AUROC, and over-diagnosis rates across different genders, hospitals and clinical use cases. However, it is also observed the underdiagnosis rate is consistently higher in the female population. In general, the female population exhibits lower incidence rates and the models perform worse when applied to this group. Furthermore, the decision curve analysis demonstrates there is no statistically significant difference between the model's clinical utility across gender groups within the interested range of thresholds. CONCLUSION The presence of gender bias within risk prediction models varies across different clinical use cases and healthcare institutions. Although inherent difference is observed between male and female populations at the data source level, this variance does not affect the parity of clinical utility. In conclusion, the evaluations conducted in this study highlight the significance of continuous monitoring of gender-based disparities in various perspectives for clinical risk prediction models.
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Affiliation(s)
| | - Hong Sun
- Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems, Jiaxing University, Jiaxing 314001, China; Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province, Jiaxing University, 314001, China.
| | | | | | - Xianchao Zhang
- Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems, Jiaxing University, Jiaxing 314001, China; Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province, Jiaxing University, 314001, China
| | - Janis Fliegenschmidt
- Institute of Anesthesiology and Pain Therapy, Heart and Diabetes Centre North Rhine, Westphalia, University Hospital of Ruhr-University Bochum, Bad Oeynhausen, Germany
| | - Nikolai Hulde
- Institute of Anesthesiology and Pain Therapy, Heart and Diabetes Centre North Rhine, Westphalia, University Hospital of Ruhr-University Bochum, Bad Oeynhausen, Germany
| | - Vera von Dossow
- Institute of Anesthesiology and Pain Therapy, Heart and Diabetes Centre North Rhine, Westphalia, University Hospital of Ruhr-University Bochum, Bad Oeynhausen, Germany
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Rampinelli V, Paderno A, Conti C, Testa G, Modesti CL, Agosti E, Dohin I, Saccardo T, Vinciguerra A, Ferrari M, Schreiber A, Mattavelli D, Nicolai P, Holsinger C, Piazza C. Artificial intelligence for automatic detection and segmentation of nasal polyposis: a pilot study. Eur Arch Otorhinolaryngol 2024:10.1007/s00405-024-08809-4. [PMID: 39001915 DOI: 10.1007/s00405-024-08809-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Accepted: 06/23/2024] [Indexed: 07/15/2024]
Abstract
PURPOSE Accurate diagnosis and quantification of polyps and symptoms are pivotal for planning the therapeutic strategy of Chronic rhinosinusitis with nasal polyposis (CRSwNP). This pilot study aimed to develop an artificial intelligence (AI)-based image analysis system capable of segmenting nasal polyps from nasal endoscopy videos. METHODS Recorded nasal videoendoscopies from 52 patients diagnosed with CRSwNP between 2019 and 2022 were retrospectively analyzed. Images extracted were manually segmented on the web application Roboflow. A dataset of 342 images was generated and divided into training (80%), validation (10%), and testing (10%) sets. The Ultralytics YOLOv8.0.28 model was employed for automated segmentation. RESULTS The YOLOv8s-seg model consisted of 195 layers and required 42.4 GFLOPs for operation. When tested against the validation set, the algorithm achieved a precision of 0.91, recall of 0.839, and mean average precision at 50% IoU (mAP50) of 0.949. For the segmentation task, similar metrics were observed, including a mAP ranging from 0.675 to 0.679 for IoUs between 50% and 95%. CONCLUSIONS The study shows that a carefully trained AI algorithm can effectively identify and delineate nasal polyps in patients with CRSwNP. Despite certain limitations like the focus on CRSwNP-specific samples, the algorithm presents a promising complementary tool to existing diagnostic methods.
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Affiliation(s)
- Vittorio Rampinelli
- Unit of Otorhinolaryngology - Head and Neck Surgery, Department of Surgical and Medical Specialties, Radiological Sciences, and Public Health, School of Medicine, ASST Spedali Civili, University of Brescia, Brescia, Italy.
| | - Alberto Paderno
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Milano, Italy
| | - Carlo Conti
- Unit of Otorhinolaryngology - Head and Neck Surgery, Department of Surgical and Medical Specialties, Radiological Sciences, and Public Health, School of Medicine, ASST Spedali Civili, University of Brescia, Brescia, Italy
| | - Gabriele Testa
- Unit of Otorhinolaryngology - Head and Neck Surgery, Department of Surgical and Medical Specialties, Radiological Sciences, and Public Health, School of Medicine, ASST Spedali Civili, University of Brescia, Brescia, Italy
| | - Claudia Lodovica Modesti
- Unit of Otorhinolaryngology - Head and Neck Surgery, Department of Surgical and Medical Specialties, Radiological Sciences, and Public Health, School of Medicine, ASST Spedali Civili, University of Brescia, Brescia, Italy
| | - Edoardo Agosti
- Division of Neurosurgery, Department of Surgical and Medical Specialties, Radiological Sciences, and Public Health, School of Medicine, ASST Spedali Civili, University of Brescia, Brescia, Italy
| | - Isabelle Dohin
- Unit of Otorhinolaryngology - Head and Neck Surgery, Department of Surgical and Medical Specialties, Radiological Sciences, and Public Health, School of Medicine, ASST Spedali Civili, University of Brescia, Brescia, Italy
| | - Tommaso Saccardo
- Section of Otorhinolaryngology - Head and Neck Surgery, Department of Neurosciences, University of Padova, Padova, PD, Italy
| | | | - Marco Ferrari
- Section of Otorhinolaryngology - Head and Neck Surgery, Department of Neurosciences, University of Padova, Padova, PD, Italy
| | - Alberto Schreiber
- Unit of Otorhinolaryngology - Head and Neck Surgery, Department of Surgical and Medical Specialties, Radiological Sciences, and Public Health, School of Medicine, ASST Spedali Civili, University of Brescia, Brescia, Italy
| | - Davide Mattavelli
- Unit of Otorhinolaryngology - Head and Neck Surgery, Department of Surgical and Medical Specialties, Radiological Sciences, and Public Health, School of Medicine, ASST Spedali Civili, University of Brescia, Brescia, Italy
| | - Piero Nicolai
- Section of Otorhinolaryngology - Head and Neck Surgery, Department of Neurosciences, University of Padova, Padova, PD, Italy
| | - Chris Holsinger
- Division of Head and Neck Surgery, Department of Otolaryngology, Stanford University, Palo Alto, CA, USA
| | - Cesare Piazza
- Unit of Otorhinolaryngology - Head and Neck Surgery, Department of Surgical and Medical Specialties, Radiological Sciences, and Public Health, School of Medicine, ASST Spedali Civili, University of Brescia, Brescia, Italy
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Sun L, Li J, Zeng S, Luo Q, Miao H, Liang Y, Cheng L, Sun Z, Tai WH, Han Y, Yin Y, Wu K, Zhang K. Artificial intelligence system for outcome evaluations of human in vitro fertilization-derived embryos. Chin Med J (Engl) 2024:00029330-990000000-01145. [PMID: 38997251 DOI: 10.1097/cm9.0000000000003162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Indexed: 07/14/2024] Open
Abstract
BACKGROUND In vitro fertilization (IVF) has emerged as a transformative solution for infertility. However, achieving favorable live-birth outcomes remains challenging. Current clinical IVF practices in IVF involve the collection of heterogeneous embryo data through diverse methods, including static images and temporal videos. However, traditional embryo selection methods, primarily reliant on visual inspection of morphology, exhibit variability and are contingent on the experience of practitioners. Therefore, an automated system that can evaluate heterogeneous embryo data to predict the final outcomes of live births is highly desirable. METHODS We employed artificial intelligence (AI) for embryo morphological grading, blastocyst embryo selection, aneuploidy prediction, and final live-birth outcome prediction. We developed and validated the AI models using multitask learning for embryo morphological assessment, including pronucleus type on day 1 and the number of blastomeres, asymmetry, and fragmentation of blastomeres on day 3, using 19,201 embryo photographs from 8271 patients. A neural network was trained on embryo and clinical metadata to identify good-quality embryos for implantation on days or day 5, and predict live-birth outcomes. Additionally, a 3D convolutional neural network was trained on 418 time-lapse videos of preimplantation genetic testing (PGT)-based ploidy outcomes for aneuploidy prediction and consequent live-birth outcomes. RESULTS These two approaches enabled us to automatically assess the implantation potential. By combining embryo and maternal metrics in an ensemble AI model, we evaluated live-birth outcomes in a prospective cohort that achieved higher accuracy than experienced embryologists (46.1% vs. 30.7% on day 3, 55.0% vs. 40.7% on day 5). Our results demonstrate the potential for AI-based selection of embryos based on characteristics beyond the observational abilities of human clinicians (area under the curve: 0.769, 95% confidence interval: 0.709-0.820). These findings could potentially provide a noninvasive, high-throughput, and low-cost screening tool to facilitate embryo selection and achieve better outcomes. CONCLUSIONS Our study underscores the AI model's ability to provide interpretable evidence for clinicians in assisted reproduction, highlighting its potential as a noninvasive, efficient, and cost-effective tool for improved embryo selection and enhanced IVF outcomes. The convergence of cutting-edge technology and reproductive medicine has opened new avenues for addressing infertility challenges and optimizing IVF success rates.
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Affiliation(s)
- Ling Sun
- Department of Reproductive Medicine, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, Guangdong 510623, China
| | - Jiahui Li
- Department of Reproductive Medicine, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, Guangdong 510623, China
- Guangzhou National Laboratory, Guangzhou, Guangdong 510000, China
| | - Simiao Zeng
- Department of Reproductive Medicine, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, Guangdong 510623, China
- Guangzhou National Laboratory, Guangzhou, Guangdong 510000, China
| | - Qiangxiang Luo
- Department of Reproductive Medicine, Jiangmen Central Hospital, Jiangmen, Guangdong 529000, China
| | - Hanpei Miao
- Guangzhou National Laboratory, Guangzhou, Guangdong 510000, China
- Department of Ophthalmology, Dongguan People's Hospital, The First School of Clinical Medicine, Southern Medical University, Dongguan, Guangdong 523000, China
| | - Yunhao Liang
- Department of Reproductive Medicine, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, Guangdong 510623, China
| | - Linling Cheng
- Institute for Artificial Intelligence in Medicine, Faculty of Medicine, Macau University of Science and Technology, Macau Special Administrative Region 999078, China
| | - Zhuo Sun
- Wenzhou Eye Hospital, Institute for Advanced Studies on Eye Health and Diseases, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
| | - Wa Hou Tai
- Institute for Artificial Intelligence in Medicine, Faculty of Medicine, Macau University of Science and Technology, Macau Special Administrative Region 999078, China
| | - Yibing Han
- Kiang Wu Hospital, Macau Special Administrative Region 999078, China
| | - Yun Yin
- Faculty of Business, City University of Macau, Macau Special Administrative Region 999078, China
| | - Keliang Wu
- State Key Laboratory of Reproductive Medicine and Offspring Health, Center for Reproductive Medicine, Institute of Women, Children and Reproductive Health and Key laboratory of Reproductive Endocrinology of Ministry of Education, Shandong University, Jinan, Shandong 250000,China
| | - Kang Zhang
- Department of Reproductive Medicine, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, Guangdong 510623, China
- Guangzhou National Laboratory, Guangzhou, Guangdong 510000, China
- Institute for Artificial Intelligence in Medicine, Faculty of Medicine, Macau University of Science and Technology, Macau Special Administrative Region 999078, China
- Wenzhou Eye Hospital, Institute for Advanced Studies on Eye Health and Diseases, Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
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Wang G, Wang K, Gao Y, Chen L, Gao T, Ma Y, Jiang Z, Yang G, Feng F, Zhang S, Gu Y, Liu G, Chen L, Ma LS, Sang Y, Xu Y, Lin G, Liu X. A generalized AI system for human embryo selection covering the entire IVF cycle via multi-modal contrastive learning. PATTERNS (NEW YORK, N.Y.) 2024; 5:100985. [PMID: 39081572 PMCID: PMC11284500 DOI: 10.1016/j.patter.2024.100985] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 03/12/2024] [Accepted: 04/10/2024] [Indexed: 08/02/2024]
Abstract
In vitro fertilization (IVF) has revolutionized infertility treatment, benefiting millions of couples worldwide. However, current clinical practices for embryo selection rely heavily on visual inspection of morphology, which is highly variable and experience dependent. Here, we propose a comprehensive artificial intelligence (AI) system that can interpret embryo-developmental knowledge encoded in vast unlabeled multi-modal datasets and provide personalized embryo selection. This AI platform consists of a transformer-based network backbone named IVFormer and a self-supervised learning framework, VTCLR (visual-temporal contrastive learning of representations), for training multi-modal embryo representations pre-trained on large and unlabeled data. When evaluated on clinical scenarios covering the entire IVF cycle, our pre-trained AI model demonstrates accurate and reliable performance on euploidy ranking and live-birth occurrence prediction. For AI vs. physician for euploidy ranking, our model achieved superior performance across all score categories. The results demonstrate the potential of the AI system as a non-invasive, efficient, and cost-effective tool to improve embryo selection and IVF outcomes.
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Affiliation(s)
- Guangyu Wang
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China
| | - Kai Wang
- College of Future Technology, Peking University and Peking-Tsinghua Center for Life Sciences, Beijing 100871, China
| | - Yuanxu Gao
- College of Future Technology, Peking University and Peking-Tsinghua Center for Life Sciences, Beijing 100871, China
| | - Longbin Chen
- Institute of Reproductive and Stem Cells, School of Basic Medicine, Central South University, Changsha, China
| | - Tianrun Gao
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China
| | - Yuanlin Ma
- Reproductive Medicine Center, the First Affiliated Hospital, Sun Yat-sen University, Guangdong, China
| | - Zeyu Jiang
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China
| | - Guoxing Yang
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China
| | - Fajin Feng
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China
| | - Shuoping Zhang
- Research Department, CITIC Xiangya Reproductive and Genetic Hospital, Changsha, China
| | - Yifan Gu
- Research Department, CITIC Xiangya Reproductive and Genetic Hospital, Changsha, China
| | - Guangdong Liu
- Department of Gynaecology and Obstetrics, The Sixth Medical Center of the General Hospital of the People’s Liberation Army, Beijing, China
| | - Lei Chen
- Department of Gynaecology and Obstetrics, The Sixth Medical Center of the General Hospital of the People’s Liberation Army, Beijing, China
| | - Li-Shuang Ma
- Capital Institute of Pediatrics, Affiliated Children’s Hospital, Beijing, China
| | - Ye Sang
- The First College of Clinical Medical Science, China Three Gorges University & Yichang Central People’s Hospital, Yichang 443003, China
| | - Yanwen Xu
- Reproductive Medicine Center, the First Affiliated Hospital, Sun Yat-sen University, Guangdong, China
| | - Ge Lin
- Institute of Reproductive and Stem Cells, School of Basic Medicine, Central South University, Changsha, China
- Research Department, CITIC Xiangya Reproductive and Genetic Hospital, Changsha, China
| | - Xiaohong Liu
- State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China
- UCL Cancer Institute, University College London, London WC1E 6BT, UK
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49
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Pati S, Kumar S, Varma A, Edwards B, Lu C, Qu L, Wang JJ, Lakshminarayanan A, Wang SH, Sheller MJ, Chang K, Singh P, Rubin DL, Kalpathy-Cramer J, Bakas S. Privacy preservation for federated learning in health care. PATTERNS (NEW YORK, N.Y.) 2024; 5:100974. [PMID: 39081567 PMCID: PMC11284498 DOI: 10.1016/j.patter.2024.100974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/02/2024]
Abstract
Artificial intelligence (AI) shows potential to improve health care by leveraging data to build models that can inform clinical workflows. However, access to large quantities of diverse data is needed to develop robust generalizable models. Data sharing across institutions is not always feasible due to legal, security, and privacy concerns. Federated learning (FL) allows for multi-institutional training of AI models, obviating data sharing, albeit with different security and privacy concerns. Specifically, insights exchanged during FL can leak information about institutional data. In addition, FL can introduce issues when there is limited trust among the entities performing the compute. With the growing adoption of FL in health care, it is imperative to elucidate the potential risks. We thus summarize privacy-preserving FL literature in this work with special regard to health care. We draw attention to threats and review mitigation approaches. We anticipate this review to become a health-care researcher's guide to security and privacy in FL.
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Affiliation(s)
- Sarthak Pati
- Center for Federated Learning in Medicine, Indiana University, Indianapolis, IN, USA
- Division of Computational Pathology, Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sourav Kumar
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Amokh Varma
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | | | - Charles Lu
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
- Center for Clinical Data Science, Massachusetts General Hospital and Brigham and Women’s Hospital, Boston, MA, USA
| | - Liangqiong Qu
- Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong, China
| | - Justin J. Wang
- Department of Biomedical Data Science, Radiology, and Medicine (Biomedical Informatics), Stanford University, Stanford, CA, USA
| | | | | | | | - Ken Chang
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Praveer Singh
- University of Colorado School of Medicine, Aurora, CO, USA
| | - Daniel L. Rubin
- Department of Biomedical Data Science, Radiology, and Medicine (Biomedical Informatics), Stanford University, Stanford, CA, USA
| | | | - Spyridon Bakas
- Center for Federated Learning in Medicine, Indiana University, Indianapolis, IN, USA
- Division of Computational Pathology, Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Neurological Surgery, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University, Indianapolis, IN, USA
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50
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Sharma A, Al-Haidose A, Al-Asmakh M, Abdallah AM. Integrating Artificial Intelligence into Biomedical Science Curricula: Advancing Healthcare Education. Clin Pract 2024; 14:1391-1403. [PMID: 39051306 PMCID: PMC11270210 DOI: 10.3390/clinpract14040112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 06/28/2024] [Accepted: 07/04/2024] [Indexed: 07/27/2024] Open
Abstract
The integration of artificial intelligence (AI) into healthcare practice has improved patient management and care. Many clinical laboratory specialties have already integrated AI in diagnostic specialties such as radiology and pathology, where it can assist in image analysis, diagnosis, and clinical reporting. As AI technologies continue to advance, it is crucial for biomedical science students to receive comprehensive education and training in AI concepts and applications and to understand the ethical consequences for such development. This review focus on the importance of integrating AI into biomedical science curricula and proposes strategies to enhance curricula for different specialties to prepare future healthcare workers. Improving the curriculum can be achieved by introducing specific subjects related to AI such as informatics, data sciences, and digital health. However, there are many challenges to enhancing the curriculum with AI. In this narrative review, we discuss these challenges and suggest mitigation strategies.
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Affiliation(s)
- Aarti Sharma
- College of Health Sciences, QU Health Sector, Qatar University, Doha 2713, Qatar
| | - Amal Al-Haidose
- Department of Biomedical Sciences, College of Health Sciences, QU Health Sector, Qatar University, Doha 2713, Qatar
| | - Maha Al-Asmakh
- Department of Biomedical Sciences, College of Health Sciences, QU Health Sector, Qatar University, Doha 2713, Qatar
| | - Atiyeh M. Abdallah
- Department of Biomedical Sciences, College of Health Sciences, QU Health Sector, Qatar University, Doha 2713, Qatar
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