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Carnino JM, Pellegrini WR, Willis M, Cohen MB, Paz-Lansberg M, Davis EM, Grillone GA, Levi JR. Assessing ChatGPT's Responses to Otolaryngology Patient Questions. Ann Otol Rhinol Laryngol 2024; 133:658-664. [PMID: 38676440 DOI: 10.1177/00034894241249621] [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: 04/28/2024]
Abstract
OBJECTIVE This study aims to evaluate ChatGPT's performance in addressing real-world otolaryngology patient questions, focusing on accuracy, comprehensiveness, and patient safety, to assess its suitability for integration into healthcare. METHODS A cross-sectional study was conducted using patient questions from the public online forum Reddit's r/AskDocs, where medical advice is sought from healthcare professionals. Patient questions were input into ChatGPT (GPT-3.5), and responses were reviewed by 5 board-certified otolaryngologists. The evaluation criteria included difficulty, accuracy, comprehensiveness, and bedside manner/empathy. Statistical analysis explored the relationship between patient question characteristics and ChatGPT response scores. Potentially dangerous responses were also identified. RESULTS Patient questions averaged 224.93 words, while ChatGPT responses were longer at 414.93 words. The accuracy scores for ChatGPT responses were 3.76/5, comprehensiveness scores were 3.59/5, and bedside manner/empathy scores were 4.28/5. Longer patient questions did not correlate with higher response ratings. However, longer ChatGPT responses scored higher in bedside manner/empathy. Higher question difficulty correlated with lower comprehensiveness. Five responses were flagged as potentially dangerous. CONCLUSION While ChatGPT exhibits promise in addressing otolaryngology patient questions, this study demonstrates its limitations, particularly in accuracy and comprehensiveness. The identification of potentially dangerous responses underscores the need for a cautious approach to AI in medical advice. Responsible integration of AI into healthcare necessitates thorough assessments of model performance and ethical considerations for patient safety.
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Affiliation(s)
- Jonathan M Carnino
- Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - William R Pellegrini
- Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Otolaryngology-Head and Neck Surgery, Boston Medical Center, Boston, MA, USA
| | - Megan Willis
- Department of Biostatistics, Boston University, Boston, MA, USA
| | - Michael B Cohen
- Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Otolaryngology-Head and Neck Surgery, Boston Medical Center, Boston, MA, USA
| | - Marianella Paz-Lansberg
- Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Otolaryngology-Head and Neck Surgery, Boston Medical Center, Boston, MA, USA
| | - Elizabeth M Davis
- Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Otolaryngology-Head and Neck Surgery, Boston Medical Center, Boston, MA, USA
| | - Gregory A Grillone
- Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Otolaryngology-Head and Neck Surgery, Boston Medical Center, Boston, MA, USA
| | - Jessica R Levi
- Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Otolaryngology-Head and Neck Surgery, Boston Medical Center, Boston, MA, USA
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Pozzi E, Velasquez DA, Varnum AA, Kava BR, Ramasamy R. Artificial Intelligence Modeling and Priapism. Curr Urol Rep 2024:10.1007/s11934-024-01221-9. [PMID: 38886246 DOI: 10.1007/s11934-024-01221-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/12/2024] [Indexed: 06/20/2024]
Abstract
PURPOSE OF REVIEW This narrative review aims to outline the current available evidence, challenges, and future perspectives of Artificial Intelligence (AI) in the diagnosis and management of priapism, a condition marked by prolonged and often painful erections that presents unique diagnostic and therapeutic challenges. RECENT FINDINGS Recent advancements in AI offer promising solutions to face the challenges in diagnosing and treating priapism. AI models have demonstrated the potential to predict the need for surgical intervention and improve diagnostic accuracy. The integration of AI models into medical decision-making for priapism can also predict long-term consequences. AI is currently being implemented in urology to enhance diagnostics and treatment work-up for various conditions, including priapism. Traditional diagnostic approaches rely heavily on assessments based on history, leading to potential delays in treatment with possible long-term sequelae. To date, the role of AI in the management of priapism is understudied, yet to achieve dependable and effective models that can reliably assist physicians in making decisions regarding both diagnostic and treatment strategies.
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Affiliation(s)
- Edoardo Pozzi
- Desai Sethi Urology Institute, Miller School of Medicine, University of Miami, Miami, FL, USA.
- University Vita-Salute San Raffaele, Milan, Italy.
- Division of Experimental Oncology, Unit of Urology, URI, IRCCS Ospedale San Raffaele, Milan, Italy.
| | - David A Velasquez
- Desai Sethi Urology Institute, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Alexandra Aponte Varnum
- Desai Sethi Urology Institute, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Bruce R Kava
- Desai Sethi Urology Institute, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Ranjith Ramasamy
- Desai Sethi Urology Institute, Miller School of Medicine, University of Miami, Miami, FL, USA
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Bangolo A, Wadhwani N, Nagesh VK, Dey S, Tran HHV, Aguilar IK, Auda A, Sidiqui A, Menon A, Daoud D, Liu J, Pulipaka SP, George B, Furman F, Khan N, Plumptre A, Sekhon I, Lo A, Weissman S. Impact of artificial intelligence in the management of esophageal, gastric and colorectal malignancies. Artif Intell Gastrointest Endosc 2024; 5:90704. [DOI: 10.37126/aige.v5.i2.90704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/28/2024] [Accepted: 03/04/2024] [Indexed: 05/11/2024] Open
Abstract
The incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate. Colorectal and gastric cancers are the third and fifth most commonly diagnosed cancers worldwide but are cited as the second and third leading causes of mortality. Early institution of appropriate therapy from timely diagnosis can optimize patient outcomes. Artificial intelligence (AI)-assisted diagnostic, prognostic, and therapeutic tools can assist in expeditious diagnosis, treatment planning/response prediction, and post-surgical prognostication. AI can intercept neoplastic lesions in their primordial stages, accurately flag suspicious and/or inconspicuous lesions with greater accuracy on radiologic, histopathological, and/or endoscopic analyses, and eliminate over-dependence on clinicians. AI-based models have shown to be on par, and sometimes even outperformed experienced gastroenterologists and radiologists. Convolutional neural networks (state-of-the-art deep learning models) are powerful computational models, invaluable to the field of precision oncology. These models not only reliably classify images, but also accurately predict response to chemotherapy, tumor recurrence, metastasis, and survival rates post-treatment. In this systematic review, we analyze the available evidence about the diagnostic, prognostic, and therapeutic utility of artificial intelligence in gastrointestinal oncology.
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Affiliation(s)
- Ayrton Bangolo
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Nikita Wadhwani
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Vignesh K Nagesh
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Shraboni Dey
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Hadrian Hoang-Vu Tran
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Izage Kianifar Aguilar
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Auda Auda
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Aman Sidiqui
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Aiswarya Menon
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Deborah Daoud
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - James Liu
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Sai Priyanka Pulipaka
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Blessy George
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Flor Furman
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Nareeman Khan
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Adewale Plumptre
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Imranjot Sekhon
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Abraham Lo
- Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
| | - Simcha Weissman
- Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
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Shi J, Chen Y, Wang Y. Deep learning and machine learning approaches to classify stomach distant metastatic tumors using DNA methylation profiles. Comput Biol Med 2024; 175:108496. [PMID: 38657466 DOI: 10.1016/j.compbiomed.2024.108496] [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: 03/09/2024] [Revised: 04/14/2024] [Accepted: 04/21/2024] [Indexed: 04/26/2024]
Abstract
Distant metastasis of cancer is a significant contributor to cancer-related complications, and early identification of unidentified stomach adenocarcinoma is crucial for a positive prognosis. Changes inDNA methylation are being increasingly recognized as a crucial factor in predicting cancer progression. Within this research, we developed machine learning and deep learning models for distinguishing distant metastasis in samples of stomach adenocarcinoma based on DNA methylation profile. Employing deep neural networks (DNN), support vector machines (SVM), random forest (RF), Naive Bayes (NB) and decision tree (DT), and models for forecasting distant metastasis in stomach adenocarcinoma. The results show that the performance of DNN is better than that of other models, AUC and AUPR achieving 99.9 % and 99.5 % respectively. Additionally, a weighted random sampling technique was utilized to address the issue of imbalanced datasets, enabling the identification of crucial methylation markers associated with functionally significant genes in stomach distant metastasis tumors with greater performance.
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Affiliation(s)
- Jing Shi
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Ying Chen
- Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Ying Wang
- Department of Endoscopy, The First Hospital of China Medical University, Shenyang, China.
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Wang Q, Liang T, Li Y, Liu X. Machine Learning for Prediction of Non-Small Cell Lung Cancer Based on Inflammatory and Nutritional Indicators in Adults: A Cross-Sectional Study. Cancer Manag Res 2024; 16:527-535. [PMID: 38832344 PMCID: PMC11146620 DOI: 10.2147/cmar.s454638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 05/23/2024] [Indexed: 06/05/2024] Open
Abstract
Purpose The aim of this study was to evaluate the potential benefit of blood inflammation in the diagnosis of non-small cell lung cancer (NSCLC) and propose a machine-learning-based method to predict NSCLC in asymptomatic adults. Patients and Methods A cross-sectional study was evaluated using medical records of 139 patients with non-small cell lung cancer and physical examination data from May 2022 to May 2023 of 198 healthy controls. The NSCLC cohort comprised 128 cases of adenocarcinoma, 3 cases of squamous cell carcinoma, and 8 cases of other NSCLC subtypes. The correlation between inflammatory and nutritional markers, such as monocytes, neutrophils, LMR, NLR, PLR, PHR and non-small cell lung cancer was examined. Features were selected using Python's feature selection library and analyzed by five algorithms. The predictive ability of the model for non-small cell lung cancer diagnosis was assessed by precision, accuracy, recall, F1 score, and area under the curve (AUC). Results The results showed that the top 14 important factors were PDW, age, TP, RBC, HGB, LYM, LYM%, RDW, PLR, LMR, PHR, MONO, MONO%, gender. Additionally, the naive Bayes (NB) algorithm demonstrated the highest overall performance in predicting adult NSCLC among the five machine learning algorithms, achieving an accuracy of 0.87, a macro average F1 score of 0.85, a weighted average F1 score of 0.87, and an AUC of 0.84. Conclusion In feature ranking, platelet distribution width was the most important feature, and the NB algorithm performed best in predicting adult NSCLC diagnosis.
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Affiliation(s)
- Qiaoli Wang
- Department of Health Screening Center, Deyang Peoples' Hospital, Deyang, Sichuan, 618000, People's Republic of China
| | - Tao Liang
- Department of Gastroenterology, Deyang Peoples' Hospital, Deyang, Sichuan, 618000, People's Republic of China
| | - Yuexi Li
- Department of Health Screening Center, Deyang Peoples' Hospital, Deyang, Sichuan, 618000, People's Republic of China
| | - Xiaoqin Liu
- Department of Health Screening Center, Deyang Peoples' Hospital, Deyang, Sichuan, 618000, People's Republic of China
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Zeng J, Zhang M, Du J, Han J, Song Q, Duan T, Yang J, Wu Y. Mortality prediction and influencing factors for intensive care unit patients with acute tubular necrosis: random survival forest and cox regression analysis. Front Pharmacol 2024; 15:1361923. [PMID: 38846097 PMCID: PMC11153709 DOI: 10.3389/fphar.2024.1361923] [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: 12/27/2023] [Accepted: 04/22/2024] [Indexed: 06/09/2024] Open
Abstract
Background: Patients with acute tubular necrosis (ATN) not only have severe renal failure, but also have many comorbidities, which can be life-threatening and require timely treatment. Identifying the influencing factors of ATN and taking appropriate interventions can effectively shorten the duration of the disease to reduce mortality and improve patient prognosis. Methods: Mortality prediction models were constructed by using the random survival forest (RSF) algorithm and the Cox regression. Next, the performance of both models was assessed by the out-of-bag (OOB) error rate, the integrated brier score, the prediction error curve, and area under the curve (AUC) at 30, 60 and 90 days. Finally, the optimal prediction model was selected and the decision curve analysis and nomogram were established. Results: RSF model was constructed under the optimal combination of parameters (mtry = 10, nodesize = 88). Vasopressors, international normalized ratio (INR)_min, chloride_max, base excess_min, bicarbonate_max, anion gap_min, and metastatic solid tumor were identified as risk factors that had strong influence on mortality in ATN patients. Uni-variate and multivariate regression analyses were used to establish the Cox regression model. Nor-epinephrine, vasopressors, INR_min, severe liver disease, and metastatic solid tumor were identified as important risk factors. The discrimination and calibration ability of both predictive models were demonstrated by the OOB error rate and the integrated brier score. However, the prediction error curve of Cox regression model was consistently lower than that of RSF model, indicating that Cox regression model was more stable and reliable. Then, Cox regression model was also more accurate in predicting mortality of ATN patients based on the AUC at different time points (30, 60 and 90 days). The analysis of decision curve analysis shows that the net benefit range of Cox regression model at different time points is large, indicating that the model has good clinical effectiveness. Finally, a nomogram predicting the risk of death was created based on Cox model. Conclusion: The Cox regression model is superior to the RSF algorithm model in predicting mortality of patients with ATN. Moreover, the model has certain clinical utility, which can provide clinicians with some reference basis in the treatment of ATN and contribute to improve patient prognosis.
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Affiliation(s)
- Jinping Zeng
- Department of Epidemiology and Health Statistics, School of Public Health, Hangzhou Normal University, Hangzhou, China
| | - Min Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Hangzhou Normal University, Hangzhou, China
| | - Jiaolan Du
- Department of Epidemiology and Health Statistics, School of Public Health, Hangzhou Normal University, Hangzhou, China
| | - Junde Han
- Department of Epidemiology and Health Statistics, School of Public Health, Hangzhou Normal University, Hangzhou, China
| | - Qin Song
- Department of Occupational and Environmental Health, School of Public Health, Hangzhou Normal University, Hangzhou, China
| | - Ting Duan
- Research on Accurate Diagnosis and Treatment of Tumor, School of Pharmacy, Hangzhou Normal University, Hangzhou, China
| | - Jun Yang
- Department of Nutrition and Toxicology, School of Public Health, Hangzhou Normal University, Hangzhou, China
| | - Yinyin Wu
- Department of Epidemiology and Health Statistics, School of Public Health, Hangzhou Normal University, Hangzhou, China
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7
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Alhuwaydi AM. Exploring the Role of Artificial Intelligence in Mental Healthcare: Current Trends and Future Directions - A Narrative Review for a Comprehensive Insight. Risk Manag Healthc Policy 2024; 17:1339-1348. [PMID: 38799612 PMCID: PMC11127648 DOI: 10.2147/rmhp.s461562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 05/10/2024] [Indexed: 05/29/2024] Open
Abstract
Mental health is an essential component of the health and well-being of a person and community, and it is critical for the individual, society, and socio-economic development of any country. Mental healthcare is currently in the health sector transformation era, with emerging technologies such as artificial intelligence (AI) reshaping the screening, diagnosis, and treatment modalities of psychiatric illnesses. The present narrative review is aimed at discussing the current landscape and the role of AI in mental healthcare, including screening, diagnosis, and treatment. Furthermore, this review attempted to highlight the key challenges, limitations, and prospects of AI in providing mental healthcare based on existing works of literature. The literature search for this narrative review was obtained from PubMed, Saudi Digital Library (SDL), Google Scholar, Web of Science, and IEEE Xplore, and we included only English-language articles published in the last five years. Keywords used in combination with Boolean operators ("AND" and "OR") were the following: "Artificial intelligence", "Machine learning", Deep learning", "Early diagnosis", "Treatment", "interventions", "ethical consideration", and "mental Healthcare". Our literature review revealed that, equipped with predictive analytics capabilities, AI can improve treatment planning by predicting an individual's response to various interventions. Predictive analytics, which uses historical data to formulate preventative interventions, aligns with the move toward individualized and preventive mental healthcare. In the screening and diagnostic domains, a subset of AI, such as machine learning and deep learning, has been proven to analyze various mental health data sets and predict the patterns associated with various mental health problems. However, limited studies have evaluated the collaboration between healthcare professionals and AI in delivering mental healthcare, as these sensitive problems require empathy, human connections, and holistic, personalized, and multidisciplinary approaches. Ethical issues, cybersecurity, a lack of data analytics diversity, cultural sensitivity, and language barriers remain concerns for implementing this futuristic approach in mental healthcare. Considering these sensitive problems require empathy, human connections, and holistic, personalized, and multidisciplinary approaches, it is imperative to explore these aspects. Therefore, future comparative trials with larger sample sizes and data sets are warranted to evaluate different AI models used in mental healthcare across regions to fill the existing knowledge gaps.
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Affiliation(s)
- Ahmed M Alhuwaydi
- Department of Internal Medicine, Division of Psychiatry, College of Medicine, Jouf University, Sakaka, Saudi Arabia
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Borna S, Gomez-Cabello CA, Pressman SM, Haider SA, Sehgal A, Leibovich BC, Cole D, Forte AJ. Comparative Analysis of Artificial Intelligence Virtual Assistant and Large Language Models in Post-Operative Care. Eur J Investig Health Psychol Educ 2024; 14:1413-1424. [PMID: 38785591 PMCID: PMC11119735 DOI: 10.3390/ejihpe14050093] [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: 04/12/2024] [Revised: 05/11/2024] [Accepted: 05/14/2024] [Indexed: 05/25/2024] Open
Abstract
In postoperative care, patient education and follow-up are pivotal for enhancing the quality of care and satisfaction. Artificial intelligence virtual assistants (AIVA) and large language models (LLMs) like Google BARD and ChatGPT-4 offer avenues for addressing patient queries using natural language processing (NLP) techniques. However, the accuracy and appropriateness of the information vary across these platforms, necessitating a comparative study to evaluate their efficacy in this domain. We conducted a study comparing AIVA (using Google Dialogflow) with ChatGPT-4 and Google BARD, assessing the accuracy, knowledge gap, and response appropriateness. AIVA demonstrated superior performance, with significantly higher accuracy (mean: 0.9) and lower knowledge gap (mean: 0.1) compared to BARD and ChatGPT-4. Additionally, AIVA's responses received higher Likert scores for appropriateness. Our findings suggest that specialized AI tools like AIVA are more effective in delivering precise and contextually relevant information for postoperative care compared to general-purpose LLMs. While ChatGPT-4 shows promise, its performance varies, particularly in verbal interactions. This underscores the importance of tailored AI solutions in healthcare, where accuracy and clarity are paramount. Our study highlights the necessity for further research and the development of customized AI solutions to address specific medical contexts and improve patient outcomes.
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Affiliation(s)
- Sahar Borna
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | | | | | - Syed Ali Haider
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Ajai Sehgal
- Center for Digital Health, Mayo Clinic, Rochester, MN 55905, USA
| | - Bradley C. Leibovich
- Center for Digital Health, Mayo Clinic, Rochester, MN 55905, USA
- Department of Urology, Mayo Clinic, Rochester, MN 55905, USA
| | - Dave Cole
- Center for Digital Health, Mayo Clinic, Rochester, MN 55905, USA
| | - Antonio Jorge Forte
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
- Center for Digital Health, Mayo Clinic, Rochester, MN 55905, USA
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Bektaş M, Tan C, Burchell GL, Daams F, van der Peet DL. Artificial intelligence-powered clinical decision making within gastrointestinal surgery: A systematic review. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2024:108385. [PMID: 38755062 DOI: 10.1016/j.ejso.2024.108385] [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: 01/19/2024] [Revised: 02/29/2024] [Accepted: 05/01/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND Clinical decision-making in gastrointestinal surgery is complex due to the unpredictability of tumoral behavior and postoperative complications. Artificial intelligence (AI) could aid in clinical decision-making by predicting these surgical outcomes. The current status of AI-based clinical decision-making within gastrointestinal surgery is unknown in recent literature. This review aims to provide an overview of AI models used for clinical decision-making within gastrointestinal surgery. METHODS A systematic literature search was performed in databases PubMed, EMBASE, Cochrane, and Web of Science. To be eligible for inclusion, studies needed to use AI models for clinical decision-making involving patients undergoing gastrointestinal surgery. Studies reporting on reviews, children, and study abstracts were excluded. The Probast risk of bias tool was used to evaluate the methodological quality of AI methods. RESULTS Out of 1073 studies, 10 articles were eligible for inclusion. AI models have been used to make clinical decisions between surgical procedures, selection of chemotherapy, selection of postoperative follow up programs, and implementation of a temporary ileostomy. Most studies have used a Random Forest or Gradient Boosting model with AUCs up to 0.97. All studies involved a retrospective study design, in which external validation was performed in one study. CONCLUSIONS This review shows that AI models have the potentiality to select the most optimal treatments for patients undergoing gastrointestinal surgery. Clinical benefits could be gained if AI models were used for clinical decision-making. However, prospective studies and randomized controlled trials will reveal the definitive role of AI models in clinical decision-making.
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Affiliation(s)
- Mustafa Bektaş
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, the Netherlands.
| | - Cevin Tan
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, the Netherlands
| | - George L Burchell
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Medical Library, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Freek Daams
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, the Netherlands
| | - Donald L van der Peet
- Amsterdam UMC Location Vrije Universiteit Amsterdam, Surgery, De Boelelaan 1117, Amsterdam, the Netherlands
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10
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Lou Y, Li D, Yu J, Chen J, Jin X. Diagnostic performance of transvaginal sonography vs. magnetic resonance imaging for rectovaginal septum deep infiltrating endometriosis: a head-to-head comparative meta-analysis. Clin Radiol 2024:S0009-9260(24)00242-3. [PMID: 38797608 DOI: 10.1016/j.crad.2024.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 04/12/2024] [Accepted: 05/03/2024] [Indexed: 05/29/2024]
Abstract
AIM We aimed to compare the diagnostic performance of transvaginal sonography (TVS) versus magnetic resonance imaging (MRI) in identifying deep infiltrating endometriosis (DIE) in the rectovaginal septum (RVS) of affected patients. MATERIALS AND METHODS An extensive search was conducted in the PubMed, Embase databases to identify available publications up to November 2023. Studies evaluating the diagnostic perfor-mance of TVS and MRI for DIE in patients with rectovaginal septum involvement were all included. Sensitivity and specificity analyses employed the DerSi-monian and Laird method, complemented by the Freeman-Tukey double arc-sine trans-formation. Additionally, the study quality was rigorously evaluated using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) method. RESULTS The meta-analysis encompassed 8 articles with a total of 721 patients. It revealed that the overall sensitivity of TVS was 0.51 (95% CI: 0.31-0.72), contrasted with 0.74 (95% CI: 0.66-0.82) for MRI. This finding suggests a higher sensitivity of MRI compared to TVS (P=0.04). Conversely, the overall specificity was 0.97 (95%CI: 0.94-1.00) for TVS and 0.93 (95% CI: 0.84-0.99) for MRI, indicating a comparable level of specificity between the two modalities (P=0.22). CONCLUSION Our meta-analysis reveals that MRI exhibits higher sensitivity and comparable specificity to TVS in patients with DIE of the RVS. However, the limited number of articles included may affect the evidence of these results. Therefore, further d number of articles included may affect the evidence of these results. Therefore, further research with larger sample sizes and prospective designs is essential to validate these findings.
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Affiliation(s)
- Y Lou
- Women Healthcare Department, CiXi Maternity & Child Health Care Hospital, Cixi 315300, Zhejiang, China
| | - D Li
- Ultrasound Department, CiXi Maternity & Child Health Care Hospital, Cixi 315300, Zhejiang, China
| | - J Yu
- Radiology Department, CiXi Maternity & Child Health Care Hospital, Cixi 315300, Zhejiang, China
| | - J Chen
- Women Healthcare Department, CiXi Maternity & Child Health Care Hospital, Cixi 315300, Zhejiang, China
| | - X Jin
- Gynecology Department, Hangzhou Women's Hospital, Hangzhou 310000, Zhejiang, China.
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Dixon D, Sattar H, Moros N, Kesireddy SR, Ahsan H, Lakkimsetti M, Fatima M, Doshi D, Sadhu K, Junaid Hassan M. Unveiling the Influence of AI Predictive Analytics on Patient Outcomes: A Comprehensive Narrative Review. Cureus 2024; 16:e59954. [PMID: 38854327 PMCID: PMC11161909 DOI: 10.7759/cureus.59954] [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] [Accepted: 05/08/2024] [Indexed: 06/11/2024] Open
Abstract
This comprehensive literature review explores the transformative impact of artificial intelligence (AI) predictive analytics on healthcare, particularly in improving patient outcomes regarding disease progression, treatment response, and recovery rates. AI, encompassing capabilities such as learning, problem-solving, and decision-making, is leveraged to predict disease progression, optimize treatment plans, and enhance recovery rates through the analysis of vast datasets, including electronic health records (EHRs), imaging, and genetic data. The utilization of machine learning (ML) and deep learning (DL) techniques in predictive analytics enables personalized medicine by facilitating the early detection of conditions, precision in drug discovery, and the tailoring of treatment to individual patient profiles. Ethical considerations, including data privacy, bias, and accountability, emerge as vital in the responsible implementation of AI in healthcare. The findings underscore the potential of AI predictive analytics in revolutionizing clinical decision-making and healthcare delivery, emphasizing the necessity of ethical guidelines and continuous model validation to ensure its safe and effective use in augmenting human judgment in medical practice.
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Affiliation(s)
- Diny Dixon
- Medicine, Jubilee Mission Medical College and Research Institute, Thrissur, IND
| | - Hina Sattar
- Medicine, Dow University of Health Sciences, Karachi, PAK
| | - Natalia Moros
- Medicine, Pontifical Javeriana University Medical School, Bogotá, COL
| | | | - Huma Ahsan
- Medicine, Jinnah Postgraduate Medical Centre, Karachi, PAK
| | | | - Madiha Fatima
- Medicine, Fatima Jinnah Medical University, Lahore, PAK
| | - Dhruvi Doshi
- Medicine, Gujarat Cancer Society Medical College, Hospital & Research Centre, Ahmedabad, IND
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12
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Atwal K. Artificial intelligence in clinical nutrition and dietetics: A brief overview of current evidence. Nutr Clin Pract 2024. [PMID: 38591653 DOI: 10.1002/ncp.11150] [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: 01/04/2024] [Revised: 03/07/2024] [Accepted: 03/13/2024] [Indexed: 04/10/2024] Open
Abstract
The rapid surge in artificial intelligence (AI) has dominated technological innovation in today's society. As experts begin to understand the potential, a spectrum of opportunities could yield a remarkable revolution. The upsurge in healthcare could transform clinical interventions and outcomes, but it risks dehumanization and increased unethical practices. The field of clinical nutrition and dietetics is no exception. This article finds a multitude of developments underway, which include the use of AI for malnutrition screening; predicting clinical outcomes, such as disease onset, and clinical risks, such as drug interactions; aiding interventions, such as estimating nutrient intake; applying precision nutrition, such as measuring postprandial glycemic response; and supporting workflow through chatbots trained on natural language models. Although the opportunity and scalability of AI is incalculably attractive, especially in the face of poor healthcare resources, the threat cannot be ignored. The risk of malpractice and lack of accountability are some of the main concerns. As such, the healthcare professional's responsibility remains paramount. The data used to train AI models could be biased, which could risk the quality of care to vulnerable or minority patient groups. Standardized AI-development protocols, benchmarked to care recommendations, with rigorous large-scale validation are required to maximize application among different settings. AI could overturn the healthcare landscape, and this article skims the surface of its potential in clinical nutrition and dietetics.
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Affiliation(s)
- Kiranjit Atwal
- Department of Nutritional Sciences, King's College London, London, UK
- School of Health Professions, University of Plymouth, Plymouth, UK
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Mathkor DM, Mathkor N, Bassfar Z, Bantun F, Slama P, Ahmad F, Haque S. Multirole of the internet of medical things (IoMT) in biomedical systems for managing smart healthcare systems: An overview of current and future innovative trends. J Infect Public Health 2024; 17:559-572. [PMID: 38367570 DOI: 10.1016/j.jiph.2024.01.013] [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: 04/06/2023] [Revised: 01/16/2024] [Accepted: 01/18/2024] [Indexed: 02/19/2024] Open
Abstract
Internet of Medical Things (IoMT) is an emerging subset of Internet of Things (IoT), often called as IoT in healthcare, refers to medical devices and applications with internet connectivity, is exponentially gaining researchers' attention due to its wide-ranging applicability in biomedical systems for Smart Healthcare systems. IoMT facilitates remote health biomedical system and plays a crucial role within the healthcare industry to enhance precision, reliability, consistency and productivity of electronic devices used for various healthcare purposes. It comprises a conceptualized architecture for providing information retrieval strategies to extract the data from patient records using sensors for biomedical analysis and diagnostics against manifold diseases to provide cost-effective medical solutions, quick hospital treatments, and personalized healthcare. This article provides a comprehensive overview of IoMT with special emphasis on its current and future trends used in biomedical systems, such as deep learning, machine learning, blockchains, artificial intelligence, radio frequency identification, and industry 5.0.
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Affiliation(s)
- Darin Mansor Mathkor
- Research and Scientific Studies Unit, Department of Nursing, College of Nursing and Health Sciences, Jazan University, Jazan 45142, Saudi Arabia
| | - Noof Mathkor
- Department of Pathology, Ministry of National Guard Health Affairs (MNGHA), Riyadh, Saudi Arabia
| | - Zaid Bassfar
- Department of Information Technology, Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi Arabia
| | - Farkad Bantun
- Department of Microbiology, Faculty of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Petr Slama
- Laboratory of Animal Immunology and Biotechnology, Department of Animal Morphology, Physiology and Genetics, Mendel University in Brno, 61300 Brno, Czech Republic
| | - Faraz Ahmad
- Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore 632014, India
| | - Shafiul Haque
- Research and Scientific Studies Unit, Department of Nursing, College of Nursing and Health Sciences, Jazan University, Jazan 45142, Saudi Arabia; Gilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, Beirut, Lebanon; Centre of Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates.
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Li W, Chen J, Chen F, Liang J, Yu H. Exploring the Potential of ChatGPT-4 in Responding to Common Questions About Abdominoplasty: An AI-Based Case Study of a Plastic Surgery Consultation. Aesthetic Plast Surg 2024; 48:1571-1583. [PMID: 37770637 DOI: 10.1007/s00266-023-03660-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 09/06/2023] [Indexed: 09/30/2023]
Abstract
BACKGROUND With the increasing integration of artificial intelligence (AI) in health care, AI chatbots like ChatGPT-4 are being used to deliver health information. OBJECTIVES This study aimed to assess the capability of ChatGPT-4 in answering common questions related to abdominoplasty, evaluating its potential as an adjunctive tool in patient education and preoperative consultation. METHODS A variety of common questions about abdominoplasty were submitted to ChatGPT-4. These questions were sourced from a question list provided by the American Society of Plastic Surgery to ensure their relevance and comprehensiveness. An experienced plastic surgeon meticulously evaluated the responses generated by ChatGPT-4 in terms of informational depth, response articulation, and competency to determine the proficiency of the AI in providing patient-centered information. RESULTS The study showed that ChatGPT-4 can give clear answers, making it useful for answering common queries. However, it struggled with personalized advice and sometimes provided incorrect or outdated references. Overall, ChatGPT-4 can effectively share abdominoplasty information, which may help patients better understand the procedure. Despite these positive findings, the AI needs more refinement, especially in providing personalized and accurate information, to fully meet patient education needs in plastic surgery. CONCLUSIONS Although ChatGPT-4 shows promise as a resource for patient education, continuous improvements and rigorous checks are essential for its beneficial integration into healthcare settings. The study emphasizes the need for further research, particularly focused on improving the personalization and accuracy of AI responses. LEVEL OF EVIDENCE V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
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Affiliation(s)
- Wenbo Li
- Department of Nursing, Jinzhou Medical University, Jinzhou, 121001, China
| | - Junjiang Chen
- Department of Burn Plastic and Medical Aesthetic Surgery, The First Affiliated Hospital, Jinzhou Medical University, Jinzhou, China
| | - Fengmin Chen
- Department of Colorectal Surgery, The First Affiliated Hospital, Jinzhou Medical University, Jinzhou, China
| | - Jiaqing Liang
- Department of Nursing, Jinzhou Medical University, Jinzhou, 121001, China
| | - Hongyu Yu
- Department of Nursing, Jinzhou Medical University, Jinzhou, 121001, China.
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Kommentar zu „Künstliche Intelligenz im Gesundheitswesen wird positiv bewertet“. Laryngorhinootologie 2024; 103:247-248. [PMID: 38565106 DOI: 10.1055/a-2204-5274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
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16
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Kim DW, Park JS, Sharma K, Velazquez A, Li L, Ostrominski JW, Tran T, Seitter Peréz RH, Shin JH. Qualitative evaluation of artificial intelligence-generated weight management diet plans. Front Nutr 2024; 11:1374834. [PMID: 38577160 PMCID: PMC10991711 DOI: 10.3389/fnut.2024.1374834] [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: 01/22/2024] [Accepted: 03/06/2024] [Indexed: 04/06/2024] Open
Abstract
Importance The transformative potential of artificial intelligence (AI), particularly via large language models, is increasingly being manifested in healthcare. Dietary interventions are foundational to weight management efforts, but whether AI techniques are presently capable of generating clinically applicable diet plans has not been evaluated. Objective Our study sought to evaluate the potential of personalized AI-generated weight-loss diet plans for clinical applications by employing a survey-based assessment conducted by experts in the fields of obesity medicine and clinical nutrition. Design setting and participants We utilized ChatGPT (4.0) to create weight-loss diet plans and selected two control diet plans from tertiary medical centers for comparison. Dietitians, physicians, and nurse practitioners specializing in obesity medicine or nutrition were invited to provide feedback on the AI-generated plans. Each plan was assessed blindly based on its effectiveness, balanced-ness, comprehensiveness, flexibility, and applicability. Personalized plans for hypothetical patients with specific health conditions were also evaluated. Main outcomes and measures The primary outcomes measured included the indistinguishability of the AI diet plan from human-created plans, and the potential of personalized AI-generated diet plans for real-world clinical applications. Results Of 95 participants, 67 completed the survey and were included in the final analysis. No significant differences were found among the three weight-loss diet plans in any evaluation category. Among the 14 experts who believed that they could identify the AI plan, only five did so correctly. In an evaluation involving 57 experts, the AI-generated personalized weight-loss diet plan was assessed, with scores above neutral for all evaluation variables. Several limitations, of the AI-generated plans were highlighted, including conflicting dietary considerations, lack of affordability, and insufficient specificity in recommendations, such as exact portion sizes. These limitations suggest that refining inputs could enhance the quality and applicability of AI-generated diet plans. Conclusion Despite certain limitations, our study highlights the potential of AI-generated diet plans for clinical applications. AI-generated dietary plans were frequently indistinguishable from diet plans widely used at major tertiary medical centers. Although further refinement and prospective studies are needed, these findings illustrate the potential of AI in advancing personalized weight-centric care.
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Affiliation(s)
- Dong Wook Kim
- Division of Endocrinology, Diabetes and Hypertension, Center for Weight Management and Wellness, Brigham and Women's Hospital, Boston, MA, United States
- Department of Medicine, Section of Endocrinology, Diabetes, Nutrition & Weight Management, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Ji Seok Park
- Department of Gastroenterology, Hepatology & Nutrition, Cleveland Clinic, Cleveland, OH, United States
| | - Kavita Sharma
- Department of Medicine, Section of Endocrinology, Diabetes, Nutrition & Weight Management, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Amanda Velazquez
- Department of Medicine, Weight Management and Metabolic Health Center, Cedars Sinai Hospital, Los Angeles, CA, United States
| | - Lu Li
- Department of Medicine, Section of Endocrinology, Diabetes, Nutrition & Weight Management, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - John W. Ostrominski
- Division of Endocrinology, Diabetes and Hypertension, Center for Weight Management and Wellness, Brigham and Women's Hospital, Boston, MA, United States
- Division of Cardiovascular Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, United States
| | - Tram Tran
- Division of Endocrinology, Diabetes and Hypertension, Center for Weight Management and Wellness, Brigham and Women's Hospital, Boston, MA, United States
- Department of Medicine, Section of Endocrinology, Diabetes, Nutrition & Weight Management, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Robert H. Seitter Peréz
- Department of Medicine, Section of Endocrinology, Diabetes, Nutrition & Weight Management, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Jeong-Hun Shin
- Department of Medicine, Section of Endocrinology, Diabetes, Nutrition & Weight Management, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Department of Internal Medicine, Hanyang University College of Medicine, Seoul, Republic of Korea
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Chen Y, Esmaeilzadeh P. Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges. J Med Internet Res 2024; 26:e53008. [PMID: 38457208 PMCID: PMC10960211 DOI: 10.2196/53008] [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: 09/22/2023] [Revised: 12/12/2023] [Accepted: 01/31/2024] [Indexed: 03/09/2024] Open
Abstract
As advances in artificial intelligence (AI) continue to transform and revolutionize the field of medicine, understanding the potential uses of generative AI in health care becomes increasingly important. Generative AI, including models such as generative adversarial networks and large language models, shows promise in transforming medical diagnostics, research, treatment planning, and patient care. However, these data-intensive systems pose new threats to protected health information. This Viewpoint paper aims to explore various categories of generative AI in health care, including medical diagnostics, drug discovery, virtual health assistants, medical research, and clinical decision support, while identifying security and privacy threats within each phase of the life cycle of such systems (ie, data collection, model development, and implementation phases). The objectives of this study were to analyze the current state of generative AI in health care, identify opportunities and privacy and security challenges posed by integrating these technologies into existing health care infrastructure, and propose strategies for mitigating security and privacy risks. This study highlights the importance of addressing the security and privacy threats associated with generative AI in health care to ensure the safe and effective use of these systems. The findings of this study can inform the development of future generative AI systems in health care and help health care organizations better understand the potential benefits and risks associated with these systems. By examining the use cases and benefits of generative AI across diverse domains within health care, this paper contributes to theoretical discussions surrounding AI ethics, security vulnerabilities, and data privacy regulations. In addition, this study provides practical insights for stakeholders looking to adopt generative AI solutions within their organizations.
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Affiliation(s)
- Yan Chen
- Department of Information Systems and Business Analytics, College of Business, Florida International University, Miami, FL, United States
| | - Pouyan Esmaeilzadeh
- Department of Information Systems and Business Analytics, College of Business, Florida International University, Miami, FL, United States
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18
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Chen J, Yuan D, Dong R, Cai J, Ai Z, Zhou S. Artificial intelligence significantly facilitates development in the mental health of college students: a bibliometric analysis. Front Psychol 2024; 15:1375294. [PMID: 38515973 PMCID: PMC10955080 DOI: 10.3389/fpsyg.2024.1375294] [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: 01/23/2024] [Accepted: 02/26/2024] [Indexed: 03/23/2024] Open
Abstract
Objective College students are currently grappling with severe mental health challenges, and research on artificial intelligence (AI) related to college students mental health, as a crucial catalyst for promoting psychological well-being, is rapidly advancing. Employing bibliometric methods, this study aim to analyze and discuss the research on AI in college student mental health. Methods Publications pertaining to AI and college student mental health were retrieved from the Web of Science core database. The distribution of publications were analyzed to gage the predominant productivity. Data on countries, authors, journal, and keywords were analyzed using VOSViewer, exploring collaboration patterns, disciplinary composition, research hotspots and trends. Results Spanning 2003 to 2023, the study encompassed 1722 publications, revealing notable insights: (1) a gradual rise in annual publications, reaching its zenith in 2022; (2) Journal of Affective Disorders and Psychiatry Research emerged were the most productive and influential sources in this field, with significant contributions from China, the United States, and their affiliated higher education institutions; (3) the primary mental health issues were depression and anxiety, with machine learning and AI having the widest range of applications; (4) an imperative for enhanced international and interdisciplinary collaboration; (5) research hotspots exploring factors influencing college student mental health and AI applications. Conclusion This study provides a succinct yet comprehensive overview of this field, facilitating a nuanced understanding of prospective applications of AI in college student mental health. Professionals can leverage this research to discern the advantages, risks, and potential impacts of AI in this critical field.
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Affiliation(s)
- Jing Chen
- Wuhan University China Institute of Boundary and Ocean Studies, Wuhan, China
| | - Dongfeng Yuan
- Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, China
| | - Ruotong Dong
- Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, China
| | - Jingyi Cai
- Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, China
| | - Zhongzhu Ai
- Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, China
- Hubei Shizhen Laboratory, Wuhan, China
| | - Shanshan Zhou
- Hubei Shizhen Laboratory, Wuhan, China
- The First Clinical Medical School, Hubei University of Chinese Medicine, Wuhan, China
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Zafar F, Fakhare Alam L, Vivas RR, Wang J, Whei SJ, Mehmood S, Sadeghzadegan A, Lakkimsetti M, Nazir Z. The Role of Artificial Intelligence in Identifying Depression and Anxiety: A Comprehensive Literature Review. Cureus 2024; 16:e56472. [PMID: 38638735 PMCID: PMC11025697 DOI: 10.7759/cureus.56472] [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] [Accepted: 03/18/2024] [Indexed: 04/20/2024] Open
Abstract
This narrative literature review undertakes a comprehensive examination of the burgeoning field, tracing the development of artificial intelligence (AI)-powered tools for depression and anxiety detection from the level of intricate algorithms to practical applications. Delivering essential mental health care services is now a significant public health priority. In recent years, AI has become a game-changer in the early identification and intervention of these pervasive mental health disorders. AI tools can potentially empower behavioral healthcare services by helping psychiatrists collect objective data on patients' progress and tasks. This study emphasizes the current understanding of AI, the different types of AI, its current use in multiple mental health disorders, advantages, disadvantages, and future potentials. As technology develops and the digitalization of the modern era increases, there will be a rise in the application of artificial intelligence in psychiatry; therefore, a comprehensive understanding will be needed. We searched PubMed, Google Scholar, and Science Direct using keywords for this. In a recent review of studies using electronic health records (EHR) with AI and machine learning techniques for diagnosing all clinical conditions, roughly 99 publications have been found. Out of these, 35 studies were identified for mental health disorders in all age groups, and among them, six studies utilized EHR data sources. By critically analyzing prominent scholarly works, we aim to illuminate the current state of this technology, exploring its successes, limitations, and future directions. In doing so, we hope to contribute to a nuanced understanding of AI's potential to revolutionize mental health diagnostics and pave the way for further research and development in this critically important domain.
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Affiliation(s)
- Fabeha Zafar
- Internal Medicine, Dow University of Health Sciences (DUHS), Karachi, PAK
| | | | - Rafael R Vivas
- Nutrition, Food and Exercise Sciences, Florida State University College of Human Sciences, Tallahassee, USA
| | - Jada Wang
- Medicine, St. George's University, Brooklyn, USA
| | - See Jia Whei
- Internal Medicine, Sriwijaya University, Palembang, IDN
| | | | | | | | - Zahra Nazir
- Internal Medicine, Combined Military Hospital, Quetta, Quetta, PAK
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Gipson DR, Chang AL, Lure AC, Mehta SA, Gowen T, Shumans E, Stevenson D, de la Cruz D, Aghaeepour N, Neu J. Reassessing acquired neonatal intestinal diseases using unsupervised machine learning. Pediatr Res 2024:10.1038/s41390-024-03074-x. [PMID: 38413766 DOI: 10.1038/s41390-024-03074-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 12/11/2023] [Accepted: 01/02/2024] [Indexed: 02/29/2024]
Abstract
BACKGROUND Acquired neonatal intestinal diseases have an array of overlapping presentations and are often labeled under the dichotomous classification of necrotizing enterocolitis (which is poorly defined) or spontaneous intestinal perforation, hindering more precise diagnosis and research. The objective of this study was to take a fresh look at neonatal intestinal disease classification using unsupervised machine learning. METHODS Patients admitted to the University of Florida Shands Neonatal Intensive Care Unit January 2013-September 2019 diagnosed with an intestinal injury, or had imaging findings of portal venous gas, pneumatosis, abdominal free air, or had an abdominal drain placed or exploratory laparotomy during admission were included. Congenital gastroschisis, omphalocele, intestinal atresia, malrotation were excluded. Data was collected via retrospective chart review with subsequent hierarchal, unsupervised clustering analysis. RESULTS Five clusters of intestinal injury were identified: Cluster 1 deemed the "Low Mortality" cluster, Cluster 2 deemed the "Mature with Inflammation" cluster, Cluster 3 deemed the "Immature with High Mortality" cluster, Cluster 4 deemed the "Late Injury at Full Feeds" cluster, and Cluster 5 deemed the "Late Injury with High Rate of Intestinal Necrosis" cluster. CONCLUSION Unsupervised machine learning can be used to cluster acquired neonatal intestinal injuries. Future study with larger multicenter datasets is needed to further refine and classify types of intestinal diseases. IMPACT Unsupervised machine learning can be used to cluster types of acquired neonatal intestinal injury. Five major clusters of acquired neonatal intestinal injury are described, each with unique features. The clusters herein described deserve future, multicenter study to determine more specific early biomarkers and tailored therapeutic interventions to improve outcomes of often devastating neonatal acquired intestinal injuries.
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Affiliation(s)
- Daniel R Gipson
- University of Florida College of Medicine, Department of Pediatrics, Division of Neonatology, Gainesville, FL, USA.
| | - Alan L Chang
- Stanford University School of Medicine, Department of Anesthesiology, Pain, and Perioperative Medicine, Department of Pediatrics, and Department of Biomedical Data Science, Stanford, CA, USA
| | - Allison C Lure
- Nationwide Children's Hospital, The Ohio State University College of Medicine, Department of Pediatrics, Division of Neonatology, Columbus, OH, USA
- University of Florida College of Medicine, Department of Pediatrics, Gainesville, FL, USA
| | - Sonia A Mehta
- University of Florida College of Medicine, Department of Pediatrics, Gainesville, FL, USA
- University of California, Irvine Medical Center, Department of Pediatrics, Division of Neonatology, Irvine, CA, USA
| | - Taylor Gowen
- University of Florida College of Medicine, Department of Pediatrics, Gainesville, FL, USA
- University of Florida College of Medicine, Department of Anesthesiology, Gainesville, FL, USA
| | - Erin Shumans
- University of Florida College of Medicine, Department of Pediatrics, Gainesville, FL, USA
| | - David Stevenson
- Stanford University School of Medicine, Department of Pediatrics, Division of Neonatology, Stanford, CA, USA
| | - Diomel de la Cruz
- University of Florida College of Medicine, Department of Pediatrics, Division of Neonatology, Gainesville, FL, USA
| | - Nima Aghaeepour
- Stanford University School of Medicine, Department of Anesthesiology, Pain, and Perioperative Medicine, Department of Pediatrics, and Department of Biomedical Data Science, Stanford, CA, USA
| | - Josef Neu
- University of Florida College of Medicine, Department of Pediatrics, Division of Neonatology, Gainesville, FL, USA
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Hasan B, Saadi S, Rajjoub NS, Hegazi M, Al-Kordi M, Fleti F, Farah M, Riaz IB, Banerjee I, Wang Z, Murad MH. Integrating large language models in systematic reviews: a framework and case study using ROBINS-I for risk of bias assessment. BMJ Evid Based Med 2024:bmjebm-2023-112597. [PMID: 38383136 DOI: 10.1136/bmjebm-2023-112597] [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] [Accepted: 02/12/2024] [Indexed: 02/23/2024]
Abstract
Large language models (LLMs) may facilitate and expedite systematic reviews, although the approach to integrate LLMs in the review process is unclear. This study evaluates GPT-4 agreement with human reviewers in assessing the risk of bias using the Risk Of Bias In Non-randomised Studies of Interventions (ROBINS-I) tool and proposes a framework for integrating LLMs into systematic reviews. The case study demonstrated that raw per cent agreement was the highest for the ROBINS-I domain of 'Classification of Intervention'. Kendall agreement coefficient was highest for the domains of 'Participant Selection', 'Missing Data' and 'Measurement of Outcomes', suggesting moderate agreement in these domains. Raw agreement about the overall risk of bias across domains was 61% (Kendall coefficient=0.35). The proposed framework for integrating LLMs into systematic reviews consists of four domains: rationale for LLM use, protocol (task definition, model selection, prompt engineering, data entry methods, human role and success metrics), execution (iterative revisions to the protocol) and reporting. We identify five basic task types relevant to systematic reviews: selection, extraction, judgement, analysis and narration. Considering the agreement level with a human reviewer in the case study, pairing artificial intelligence with an independent human reviewer remains required.
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Affiliation(s)
- Bashar Hasan
- Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota, USA
- Public Health, Infectious Diseases and Occupational Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Samer Saadi
- Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota, USA
- Public Health, Infectious Diseases and Occupational Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Noora S Rajjoub
- Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota, USA
| | - Moustafa Hegazi
- Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota, USA
- Public Health, Infectious Diseases and Occupational Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Mohammad Al-Kordi
- Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota, USA
- Public Health, Infectious Diseases and Occupational Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Farah Fleti
- Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota, USA
- Public Health, Infectious Diseases and Occupational Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Magdoleen Farah
- Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota, USA
- Public Health, Infectious Diseases and Occupational Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Irbaz B Riaz
- Division of Hematology-Oncology Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Imon Banerjee
- Department of Radiology, Mayo Clinic Arizona, Scottsdale, Arizona, USA
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, Arizona, USA
| | - Zhen Wang
- Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota, USA
- Health Care Policy and Research, Mayo Clinic Minnesota, Rochester, Minnesota, USA
| | - Mohammad Hassan Murad
- Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, Minnesota, USA
- Public Health, Infectious Diseases and Occupational Medicine, Mayo Clinic, Rochester, Minnesota, USA
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Wu CC, Islam MM, Poly TN, Weng YC. Artificial Intelligence in Kidney Disease: A Comprehensive Study and Directions for Future Research. Diagnostics (Basel) 2024; 14:397. [PMID: 38396436 PMCID: PMC10887584 DOI: 10.3390/diagnostics14040397] [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: 12/04/2023] [Revised: 02/03/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a promising tool in the field of healthcare, with an increasing number of research articles evaluating its applications in the domain of kidney disease. To comprehend the evolving landscape of AI research in kidney disease, a bibliometric analysis is essential. The purposes of this study are to systematically analyze and quantify the scientific output, research trends, and collaborative networks in the application of AI to kidney disease. This study collected AI-related articles published between 2012 and 20 November 2023 from the Web of Science. Descriptive analyses of research trends in the application of AI in kidney disease were used to determine the growth rate of publications by authors, journals, institutions, and countries. Visualization network maps of country collaborations and author-provided keyword co-occurrences were generated to show the hotspots and research trends in AI research on kidney disease. The initial search yielded 673 articles, of which 631 were included in the analyses. Our findings reveal a noteworthy exponential growth trend in the annual publications of AI applications in kidney disease. Nephrology Dialysis Transplantation emerged as the leading publisher, accounting for 4.12% (26 out of 631 papers), followed by the American Journal of Transplantation at 3.01% (19/631) and Scientific Reports at 2.69% (17/631). The primary contributors were predominantly from the United States (n = 164, 25.99%), followed by China (n = 156, 24.72%) and India (n = 62, 9.83%). In terms of institutions, Mayo Clinic led with 27 contributions (4.27%), while Harvard University (n = 19, 3.01%) and Sun Yat-Sen University (n = 16, 2.53%) secured the second and third positions, respectively. This study summarized AI research trends in the field of kidney disease through statistical analysis and network visualization. The findings show that the field of AI in kidney disease is dynamic and rapidly progressing and provides valuable information for recognizing emerging patterns, technological shifts, and interdisciplinary collaborations that contribute to the advancement of knowledge in this critical domain.
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Affiliation(s)
- Chieh-Chen Wu
- Department of Healthcare Information and Management, School of Health and Medical Engineering, Ming Chuan University, Taipei 111, Taiwan;
| | - Md. Mohaimenul Islam
- Outcomes and Translational Sciences, College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA;
| | - Tahmina Nasrin Poly
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan;
| | - Yung-Ching Weng
- Department of Healthcare Information and Management, School of Health and Medical Engineering, Ming Chuan University, Taipei 111, Taiwan;
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23
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Nyquist ML, Fink LA, Mauldin GE, Coffman CR. Evaluation of a Novel Veterinary Dental Radiography Artificial Intelligence Software Program. J Vet Dent 2024:8987564231221071. [PMID: 38321886 DOI: 10.1177/08987564231221071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
There is a growing trend of artificial intelligence (AI) applications in veterinary medicine, with the potential to assist veterinarians in clinical decisions. A commercially available, AI-based software program (AISP) for detecting common radiographic dental pathologies in dogs and cats was assessed for agreement with two human evaluators. Furcation bone loss, periapical lucency, resorptive lesion, retained tooth root, attachment (alveolar bone) loss and tooth fracture were assessed. The AISP does not attempt to diagnose or provide treatment recommendations, nor has it been trained to identify other types of radiographic pathology. Inter-rater reliability for detecting pathologies was measured by absolute percent agreement and Gwet's agreement coefficient. There was good to excellent inter-rater reliability among all raters, suggesting the AISP performs similarly at detecting the specified pathologies compared to human evaluators. Sensitivity and specificity for the AISP were assessed using human evaluators as the reference standard. The results revealed a trend of low sensitivity and high specificity, suggesting the AISP may produce a high rate of false negatives and may not be a good tool for initial screening. However, the low rate of false positives produced by the AISP suggests it may be beneficial as a "second set of eyes" because if it detects the specific pathology, there is a high likelihood that the pathology is present. With an understanding of the AISP, as an aid and not a substitute for veterinarians, the technology may increase dental radiography utilization and diagnostic potential.
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Affiliation(s)
| | - Lisa A Fink
- Arizona Veterinary Dental Specialists, Scottsdale, AZ, USA
| | | | - Curt R Coffman
- Arizona Veterinary Dental Specialists, Scottsdale, AZ, USA
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24
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Lin SC, Chandra E, Tsao PN, Liao WC, Chen WJ, Yen TA, Hsu JYJ, Jeng SF. Application of Artificial Intelligence in Infant Movement Classification: A Reliability and Validity Study in Infants Who Were Full-Term and Preterm. Phys Ther 2024; 104:pzad176. [PMID: 38245806 DOI: 10.1093/ptj/pzad176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 10/18/2023] [Accepted: 12/03/2023] [Indexed: 01/22/2024]
Abstract
OBJECTIVE Preterm infants are at high risk of neuromotor disorders. Recent advances in digital technology and machine learning algorithms have enabled the tracking and recognition of anatomical key points of the human body. It remains unclear whether the proposed pose estimation model and the skeleton-based action recognition model for adult movement classification are applicable and accurate for infant motor assessment. Therefore, this study aimed to develop and validate an artificial intelligence (AI) model framework for movement recognition in full-term and preterm infants. METHODS This observational study prospectively assessed 30 full-term infants and 54 preterm infants using the Alberta Infant Motor Scale (58 movements) from 4 to 18 months of age with their movements recorded by 5 video cameras simultaneously in a standardized clinical setup. The movement videos were annotated for the start/end times and presence of movements by 3 pediatric physical therapists. The annotated videos were used for the development and testing of an AI algorithm that consisted of a 17-point human pose estimation model and a skeleton-based action recognition model. RESULTS The infants contributed 153 sessions of Alberta Infant Motor Scale assessment that yielded 13,139 videos of movements for data processing. The intra and interrater reliabilities for movement annotation of videos by the therapists showed high agreements (88%-100%). Thirty-one of the 58 movements were selected for machine learning because of sufficient data samples and developmental significance. Using the annotated results as the standards, the AI algorithm showed satisfactory agreement in classifying the 31 movements (accuracy = 0.91, recall = 0.91, precision = 0.91, and F1 score = 0.91). CONCLUSION The AI algorithm was accurate in classifying 31 movements in full-term and preterm infants from 4 to 18 months of age in a standardized clinical setup. IMPACT The findings provide the basis for future refinement and validation of the algorithm on home videos to be a remote infant movement assessment.
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Affiliation(s)
- Shiang-Chin Lin
- School and Graduate Institute of Physical Therapy, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Erick Chandra
- Department of Computer Science, National Taiwan University College of Electric Engineering and Computer Science, Taipei, Taiwan
| | - Po Nien Tsao
- Department of Pediatrics, National Taiwan University Hospital, Taipei, Taiwan
| | - Wei-Chih Liao
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Wei-J Chen
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
| | - Ting-An Yen
- Department of Pediatrics, National Taiwan University Hospital, Taipei, Taiwan
| | - Jane Yung-Jen Hsu
- Department of Computer Science, National Taiwan University College of Electric Engineering and Computer Science, Taipei, Taiwan
| | - Suh-Fang Jeng
- School and Graduate Institute of Physical Therapy, National Taiwan University College of Medicine, Taipei, Taiwan
- Physical Therapy Center, National Taiwan University Hospital, Taipei, Taiwan
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25
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Aamir A, Iqbal A, Jawed F, Ashfaque F, Hafsa H, Anas Z, Oduoye MO, Basit A, Ahmed S, Abdul Rauf S, Khan M, Mansoor T. Exploring the current and prospective role of artificial intelligence in disease diagnosis. Ann Med Surg (Lond) 2024; 86:943-949. [PMID: 38333305 PMCID: PMC10849462 DOI: 10.1097/ms9.0000000000001700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 12/28/2023] [Indexed: 02/10/2024] Open
Abstract
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems, providing assistance in a variety of patient care and health systems. The aim of this review is to contribute valuable insights to the ongoing discourse on the transformative potential of AI in healthcare, providing a nuanced understanding of its current applications, future possibilities, and associated challenges. The authors conducted a literature search on the current role of AI in disease diagnosis and its possible future applications using PubMed, Google Scholar, and ResearchGate within 10 years. Our investigation revealed that AI, encompassing machine-learning and deep-learning techniques, has become integral to healthcare, facilitating immediate access to evidence-based guidelines, the latest medical literature, and tools for generating differential diagnoses. However, our research also acknowledges the limitations of current AI methodologies in disease diagnosis and explores uncertainties and obstacles associated with the complete integration of AI into clinical practice. This review has highlighted the critical significance of integrating AI into the medical healthcare framework and meticulously examined the evolutionary trajectory of healthcare-oriented AI from its inception, delving into the current state of development and projecting the extent of reliance on AI in the future. The authors have found that central to this study is the exploration of how the strategic integration of AI can accelerate the diagnostic process, heighten diagnostic accuracy, and enhance overall operational efficiency, concurrently relieving the burdens faced by healthcare practitioners.
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Affiliation(s)
- Ali Aamir
- Department of Medicine, Dow University of Health Sciences
| | - Arham Iqbal
- Department of Medicine, Dow International Medical College, Karachi, Pakistan
| | - Fareeha Jawed
- Department of Medicine, Dow University of Health Sciences
| | - Faiza Ashfaque
- Department of Medicine, Dow University of Health Sciences
| | - Hafiza Hafsa
- Department of Medicine, Dow University of Health Sciences
| | - Zahra Anas
- Department of Medicine, Dow University of Health Sciences
| | - Malik Olatunde Oduoye
- Department of Research, Medical Research Circle, Bukavu, Democratic Republic of Congo
| | - Abdul Basit
- Department of Medicine, Dow University of Health Sciences
| | - Shaheer Ahmed
- Department of Medicine, Dow University of Health Sciences
| | | | - Mushkbar Khan
- Liaquat National Hospital and Medical College, Pakistan
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26
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Huang Y, Cheung CY, Li D, Tham YC, Sheng B, Cheng CY, Wang YX, Wong TY. AI-integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook. Eye (Lond) 2024; 38:464-472. [PMID: 37709926 PMCID: PMC10858189 DOI: 10.1038/s41433-023-02724-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 07/26/2023] [Accepted: 08/25/2023] [Indexed: 09/16/2023] Open
Abstract
Cardiovascular disease (CVD) remains the leading cause of death worldwide. Assessing of CVD risk plays an essential role in identifying individuals at higher risk and enables the implementation of targeted intervention strategies, leading to improved CVD prevalence reduction and patient survival rates. The ocular vasculature, particularly the retinal vasculature, has emerged as a potential means for CVD risk stratification due to its anatomical similarities and physiological characteristics shared with other vital organs, such as the brain and heart. The integration of artificial intelligence (AI) into ocular imaging has the potential to overcome limitations associated with traditional semi-automated image analysis, including inefficiency and manual measurement errors. Furthermore, AI techniques may uncover novel and subtle features that contribute to the identification of ocular biomarkers associated with CVD. This review provides a comprehensive overview of advancements made in AI-based ocular image analysis for predicting CVD, including the prediction of CVD risk factors, the replacement of traditional CVD biomarkers (e.g., CT-scan measured coronary artery calcium score), and the prediction of symptomatic CVD events. The review covers a range of ocular imaging modalities, including colour fundus photography, optical coherence tomography, and optical coherence tomography angiography, and other types of images like external eye images. Additionally, the review addresses the current limitations of AI research in this field and discusses the challenges associated with translating AI algorithms into clinical practice.
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Affiliation(s)
- Yu Huang
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Dawei Li
- College of Future Technology, Peking University, Beijing, China
| | - Yih Chung Tham
- Centre for Innovation and Precision Eye Health and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Bin Sheng
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ching Yu Cheng
- Centre for Innovation and Precision Eye Health and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore.
- Tsinghua Medicine, Tsinghua University, Beijing, China.
- School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Beijing, China.
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27
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Kapsali MZ, Livanis E, Tsalikidis C, Oikonomou P, Voultsos P, Tsaroucha A. Ethical Concerns About ChatGPT in Healthcare: A Useful Tool or the Tombstone of Original and Reflective Thinking? Cureus 2024; 16:e54759. [PMID: 38523987 PMCID: PMC10961144 DOI: 10.7759/cureus.54759] [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] [Accepted: 02/23/2024] [Indexed: 03/26/2024] Open
Abstract
Artificial intelligence (AI), the uprising technology of computer science aiming to create digital systems with human behavior and intelligence, seems to have invaded almost every field of modern life. Launched in November 2022, ChatGPT (Chat Generative Pre-trained Transformer) is a textual AI application capable of creating human-like responses characterized by original language and high coherence. Although AI-based language models have demonstrated impressive capabilities in healthcare, ChatGPT has received controversial annotations from the scientific and academic communities. This chatbot already appears to have a massive impact as an educational tool for healthcare professionals and transformative potential for clinical practice and could lead to dramatic changes in scientific research. Nevertheless, rational concerns were raised regarding whether the pre-trained, AI-generated text would be a menace not only for original thinking and new scientific ideas but also for academic and research integrity, as it gets more and more difficult to distinguish its AI origin due to the coherence and fluency of the produced text. This short review aims to summarize the potential applications and the consequential implications of ChatGPT in the three critical pillars of medicine: education, research, and clinical practice. In addition, this paper discusses whether the current use of this chatbot is in compliance with the ethical principles for the safe use of AI in healthcare, as determined by the World Health Organization. Finally, this review highlights the need for an updated ethical framework and the increased vigilance of healthcare stakeholders to harvest the potential benefits and limit the imminent dangers of this new innovative technology.
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Affiliation(s)
- Marina Z Kapsali
- Postgraduate Program on Bioethics, Laboratory of Bioethics, Democritus University of Thrace, Alexandroupolis, GRC
| | - Efstratios Livanis
- Department of Accounting and Finance, University of Macedonia, Thessaloniki, GRC
| | - Christos Tsalikidis
- Department of General Surgery, Democritus University of Thrace, Alexandroupolis, GRC
| | - Panagoula Oikonomou
- Laboratory of Experimental Surgery, Department of General Surgery, Democritus University of Thrace, Alexandroupolis, GRC
| | - Polychronis Voultsos
- Laboratory of Forensic Medicine & Toxicology (Medical Law and Ethics), School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, GRC
| | - Aleka Tsaroucha
- Department of General Surgery, Democritus University of Thrace, Alexandroupolis, GRC
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28
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Ben Yellin, Lahav C, Sela I, Yahalom G, Shoval SR, Elon Y, Fuller J, Harel M. Analytical validation of the PROphet test for treatment decision-making guidance in metastatic non-small cell lung cancer. J Pharm Biomed Anal 2024; 238:115803. [PMID: 37871417 DOI: 10.1016/j.jpba.2023.115803] [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: 08/07/2023] [Revised: 09/22/2023] [Accepted: 10/16/2023] [Indexed: 10/25/2023]
Abstract
The blood proteome, consisting of thousands of proteins engaged in various biological processes, acts as a valuable source of potential biomarkers for various medical applications. PROphet is a plasma proteomics-based test that serves as a decision-support tool for non-small cell lung cancer (NSCLC) patients, combining proteomic profiling using SomaScan technology and subsequent computational algorithm. PROphet was implemented as a laboratory developed test (LDT). Under the Clinical Laboratory Improvement Amendments (CLIA) and Commission on Office Laboratory Accreditation (COLA) regulations, prior to releasing patient test results, a clinical laboratory located in the United States employing an LDT must examine its performance characteristics with regard to analytical validity. This study describes the experimental and computational analytical validity of the PROphet test, as required by CLIA/COLA regulations. Experimental precision analysis displayed a median coefficient of variation (CV) of 3.9 % and 4.7 % for intra-plate and inter-plate examination, respectively, and the median accuracy rate between sites was 88 %. Computational precision exhibited a high accuracy rate, with 93 % of samples displaying complete concordance in results. A cross-platform comparison between SomaScan and other proteomics platforms yielded a median Spearman's rank correlation coefficient of 0.51, affirming the consistency and reliability of the SomaScan platform as used under the PROphet test. Our study presents a robust framework for evaluating the analytical validity of a platform that combines an experimental assay with subsequent computational algorithms. When applied to the PROphet test, strong analytical performance of the test was demonstrated.
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Affiliation(s)
- Ben Yellin
- OncoHost LTD, Hamelacha 17 Binyamina, 3057324, Israel
| | - Coren Lahav
- OncoHost LTD, Hamelacha 17 Binyamina, 3057324, Israel
| | - Itamar Sela
- OncoHost LTD, Hamelacha 17 Binyamina, 3057324, Israel
| | - Galit Yahalom
- OncoHost LTD, Hamelacha 17 Binyamina, 3057324, Israel
| | | | | | - James Fuller
- OncoHost Inc., 1110 SE Cary Parkway, Suite 205, Cary, NC 27518, USA
| | - Michal Harel
- OncoHost LTD, Hamelacha 17 Binyamina, 3057324, Israel.
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29
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Wang S, Ono R, Wu D, Aoki K, Kato H, Iwahana T, Okada S, Kobayashi Y, Liu H. Pulse wave-based evaluation of the blood-supply capability of patients with heart failure via machine learning. Biomed Eng Online 2024; 23:7. [PMID: 38243221 PMCID: PMC10797936 DOI: 10.1186/s12938-024-01201-7] [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: 08/18/2023] [Accepted: 01/04/2024] [Indexed: 01/21/2024] Open
Abstract
Pulse wave, as a message carrier in the cardiovascular system (CVS), enables inferring CVS conditions while diagnosing cardiovascular diseases (CVDs). Heart failure (HF) is a major CVD, typically requiring expensive and time-consuming treatments for health monitoring and disease deterioration; it would be an effective and patient-friendly tool to facilitate rapid and precise non-invasive evaluation of the heart's blood-supply capability by means of powerful feature-abstraction capability of machine learning (ML) based on pulse wave, which remains untouched yet. Here we present an ML-based methodology, which is verified to accurately evaluate the blood-supply capability of patients with HF based on clinical data of 237 patients, enabling fast prediction of five representative cardiovascular function parameters comprising left ventricular ejection fraction (LVEF), left ventricular end-diastolic diameter (LVDd), left ventricular end-systolic diameter (LVDs), left atrial dimension (LAD), and peripheral oxygen saturation (SpO2). Two ML networks were employed and optimized based on high-quality pulse wave datasets, and they were validated consistently through statistical analysis based on the summary independent-samples t-test (p > 0.05), the Bland-Altman analysis with clinical measurements, and the error-function analysis. It is proven that evaluation of the SpO2, LAD, and LVDd performance can be achieved with the maximum error < 15%. While our findings thus demonstrate the potential of pulse wave-based, non-invasive evaluation of the blood-supply capability of patients with HF, they also set the stage for further refinements in health monitoring and deterioration prevention applications.
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Affiliation(s)
- Sirui Wang
- Graduate School of Science and Engineering, Chiba University, Chiba, Japan
| | - Ryohei Ono
- Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Dandan Wu
- Graduate School of Science and Engineering, Chiba University, Chiba, Japan
| | - Kaoruko Aoki
- Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Hirotoshi Kato
- Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Togo Iwahana
- Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Sho Okada
- Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Yoshio Kobayashi
- Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Hao Liu
- Graduate School of Science and Engineering, Chiba University, Chiba, Japan.
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30
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Rony MKK, Parvin MR, Ferdousi S. Advancing nursing practice with artificial intelligence: Enhancing preparedness for the future. Nurs Open 2024; 11:10.1002/nop2.2070. [PMID: 38268252 PMCID: PMC10733565 DOI: 10.1002/nop2.2070] [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: 06/10/2023] [Revised: 11/15/2023] [Accepted: 12/02/2023] [Indexed: 01/26/2024] Open
Abstract
AIM This article aimed to explore the role of AI in advancing nursing practice, focusing on its impact on readiness for the future. DESIGN AND METHODS A position paper, the methodology comprises three key steps. First, a comprehensive literature search using specific keywords in reputable databases was conducted to gather current information on AI in nursing. Second, data extraction and synthesis from selected articles were performed. Finally, a thematic analysis identifies recurring themes to provide insights into AI's impact on future nursing practice. RESULTS The findings highlight the transformative role of AI in advancing nursing practice and preparing nurses for the future, including enhancing nursing practice with AI, preparing nurses for the future (AI education and training) and associated, ethical considerations and challenges. AI-enabled robotics and telehealth solutions expand the reach of nursing care, improving accessibility of healthcare services and remote monitoring capabilities of patients' health conditions.
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Affiliation(s)
| | - Mst. Rina Parvin
- Major of Bangladesh ArmyCombined Military HospitalDhakaBangladesh
| | - Silvia Ferdousi
- International University of Business Agriculture and TechnologyDhakaBangladesh
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31
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Yoon M, Park JJ, Hur T, Hua CH, Hussain M, Lee S, Choi DJ. Application and Potential of Artificial Intelligence in Heart Failure: Past, Present, and Future. INTERNATIONAL JOURNAL OF HEART FAILURE 2024; 6:11-19. [PMID: 38303917 PMCID: PMC10827704 DOI: 10.36628/ijhf.2023.0050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/24/2023] [Accepted: 11/26/2023] [Indexed: 02/03/2024]
Abstract
The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.
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Affiliation(s)
- Minjae Yoon
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Jin Joo Park
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Taeho Hur
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
- Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea
| | - Cam-Hao Hua
- Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea
| | - Musarrat Hussain
- Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea
| | - Sungyoung Lee
- Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea
| | - Dong-Ju Choi
- Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
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32
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Bednorz A, Mak JKL, Jylhävä J, Religa D. Use of Electronic Medical Records (EMR) in Gerontology: Benefits, Considerations and a Promising Future. Clin Interv Aging 2023; 18:2171-2183. [PMID: 38152074 PMCID: PMC10752027 DOI: 10.2147/cia.s400887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 11/05/2023] [Indexed: 12/29/2023] Open
Abstract
Electronic medical records (EMRs) have many benefits in clinical research in gerontology, enabling data analysis, development of prognostic tools and disease risk prediction. EMRs also offer a range of advantages in clinical practice, such as comprehensive medical records, streamlined communication with healthcare providers, remote data access, and rapid retrieval of test results, ultimately leading to increased efficiency, enhanced patient safety, and improved quality of care in gerontology, which includes benefits like reduced medication use and better patient history taking and physical examination assessments. The use of artificial intelligence (AI) and machine learning (ML) approaches on EMRs can further improve disease diagnosis, symptom classification, and support clinical decision-making. However, there are also challenges related to data quality, data entry errors, as well as the ethics and safety of using AI in healthcare. This article discusses the future of EMRs in gerontology and the application of AI and ML in clinical research. Ethical and legal issues surrounding data sharing and the need for healthcare professionals to critically evaluate and integrate these technologies are also emphasized. The article concludes by discussing the challenges related to the use of EMRs in research as well as in their primary intended use, the daily clinical practice.
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Affiliation(s)
- Adam Bednorz
- John Paul II Geriatric Hospital, Katowice, Poland
- Institute of Psychology, Humanitas Academy, Sosnowiec, Poland
| | - Jonathan K L Mak
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Juulia Jylhävä
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Faculty of Social Sciences (Health Sciences) and Gerontology Research Center (GEREC), University of Tampere, Tampere, Finland
| | - Dorota Religa
- Division of Clinical Geriatrics, Department of Neurobiology, Care sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Theme Inflammation and Aging, Karolinska University Hospital, Huddinge, Sweden
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Feng L, Xu J, Ji X, Chen L, Xing S, Liu B, Han J, Zhao K, Li J, Xia S, Guan J, Yan C, Tong Q, Long H, Zhang J, Chen R, Tian D, Luo X, Xiao F, Liao J. Development and validation of a three-dimensional deep learning-based system for assessing bowel preparation on colonoscopy video. Front Med (Lausanne) 2023; 10:1296249. [PMID: 38164219 PMCID: PMC10757977 DOI: 10.3389/fmed.2023.1296249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 12/01/2023] [Indexed: 01/03/2024] Open
Abstract
Background The performance of existing image-based training models in evaluating bowel preparation on colonoscopy videos was relatively low, and only a few models used external data to prove their generalization. Therefore, this study attempted to develop a more precise and stable AI system for assessing bowel preparation of colonoscopy video. Methods We proposed a system named ViENDO to assess the bowel preparation quality, including two CNNs. First, Information-Net was used to identify and filter out colonoscopy video frames unsuitable for Boston bowel preparation scale (BBPS) scoring. Second, BBPS-Net was trained and tested with 5,566 suitable short video clips through three-dimensional (3D) convolutional neural network (CNN) technology to detect BBPS-based insufficient bowel preparation. Then, ViENDO was applied to complete withdrawal colonoscopy videos from multiple centers to predict BBPS segment scores in clinical settings. We also conducted a human-machine contest to compare its performance with endoscopists. Results In video clips, BBPS-Net for determining inadequate bowel preparation generated an area under the curve of up to 0.98 and accuracy of 95.2%. When applied to full-length withdrawal colonoscopy videos, ViENDO assessed bowel cleanliness with an accuracy of 93.8% in the internal test set and 91.7% in the external dataset. The human-machine contest demonstrated that the accuracy of ViENDO was slightly superior compared to most endoscopists, though no statistical significance was found. Conclusion The 3D-CNN-based AI model showed good performance in evaluating full-length bowel preparation on colonoscopy video. It has the potential as a substitute for endoscopists to provide BBPS-based assessments during daily clinical practice.
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Affiliation(s)
- Lina Feng
- Department of Gastroenterology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiaxin Xu
- Department of Gastroenterology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xuantao Ji
- Wuhan United Imaging Healthcare Surgical Technology Co., Ltd., Wuhan, China
| | - Liping Chen
- Department of Gastroenterology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shuai Xing
- Department of Gastroenterology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Bo Liu
- Wuhan United Imaging Healthcare Surgical Technology Co., Ltd., Wuhan, China
| | - Jian Han
- Department of Gastroenterology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kai Zhao
- Department of Gastroenterology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Junqi Li
- Changzhou United Imaging Healthcare Surgical Technology Co., Ltd., Changzhou, China
| | - Suhong Xia
- Department of Gastroenterology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jialun Guan
- Department of Gastroenterology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chenyu Yan
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Qiaoyun Tong
- Department of Gastroenterology, Yichang Central People’s Hospital, China Three Gorges University, Yichang, China
| | - Hui Long
- Department of Gastroenterology, Tianyou Hospital, Wuhan University of Science and Technology, Wuhan, China
| | - Juanli Zhang
- Department of Gastroenterology, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China
- Department of Gastroenterology, Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, China
| | - Ruihong Chen
- Department of Gastroenterology, Xiantao First People’s Hospital Affiliated to Yangtze University, Wuhan, China
| | - Dean Tian
- Department of Gastroenterology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoping Luo
- Department of Pediatrics, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fang Xiao
- Department of Gastroenterology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiazhi Liao
- Department of Gastroenterology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Calcote MJ, Mann JR, Adcock KG, Duckworth S, Donald MC. Big Data in Health Care: An Interprofessional Course. Nurse Educ 2023:00006223-990000000-00374. [PMID: 37994454 DOI: 10.1097/nne.0000000000001571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2023]
Abstract
BACKGROUND The widespread adoption of the electronic health record (EHR) has resulted in vast repositories of EHR big data that are being used to identify patterns and correlations that translate into data-informed health care decision making. PROBLEM Health care professionals need the skills necessary to navigate a digitized, data-rich health care environment as big data plays an increasingly integral role in health care. APPROACH Faculty incorporated the concept of big data in an asynchronous online course allowing an interprofessional mix of students to analyze EHR big data on over a million patients. OUTCOMES Students conducted a descriptive analysis of cohorts of patients with selected diagnoses and presented their findings. CONCLUSIONS Students collaborated with an interprofessional team to analyze EHR big data on selected variables. The teams used data visualization tools to describe an assigned diagnosis patient population.
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Affiliation(s)
- Margaret J Calcote
- Assistant Professor (Dr Calcote), The University of Mississippi Medical Center School of Nursing, Jackson; Professor and Chair (Dr Mann), Department of Preventive Medicine, The University of Mississippi Medical Center School of Medicine, Jackson; Professor (Dr Adcock), Pharmacy Division, The University of Mississippi Medical Center School of Pharmacy, Jackson; Professor (Dr Duckworth), The University of Mississippi Medical Center Division of Internal Medicine, Jackson; and Medical Student M3 (Mr Donald), The University of Mississippi Medical Center School of Medicine, Jackson
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Souza R, Wilms M, Camacho M, Pike GB, Camicioli R, Monchi O, Forkert ND. Image-encoded biological and non-biological variables may be used as shortcuts in deep learning models trained on multisite neuroimaging data. J Am Med Inform Assoc 2023; 30:1925-1933. [PMID: 37669158 PMCID: PMC10654841 DOI: 10.1093/jamia/ocad171] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 08/07/2023] [Accepted: 08/15/2023] [Indexed: 09/07/2023] Open
Abstract
OBJECTIVE This work investigates if deep learning (DL) models can classify originating site locations directly from magnetic resonance imaging (MRI) scans with and without correction for intensity differences. MATERIAL AND METHODS A large database of 1880 T1-weighted MRI scans collected across 41 sites originally for Parkinson's disease (PD) classification was used to classify sites in this study. Forty-six percent of the datasets are from PD patients, while 54% are from healthy participants. After preprocessing the T1-weighted scans, 2 additional data types were generated: intensity-harmonized T1-weighted scans and log-Jacobian deformation maps resulting from nonlinear atlas registration. Corresponding DL models were trained to classify sites for each data type. Additionally, logistic regression models were used to investigate the contribution of biological (age, sex, disease status) and non-biological (scanner type) variables to the models' decision. RESULTS A comparison of the 3 different types of data revealed that DL models trained using T1-weighted and intensity-harmonized T1-weighted scans can classify sites with an accuracy of 85%, while the model using log-Jacobian deformation maps achieved a site classification accuracy of 54%. Disease status and scanner type were found to be significant confounders. DISCUSSION Our results demonstrate that MRI scans encode relevant site-specific information that models could use as shortcuts that cannot be removed using simple intensity harmonization methods. CONCLUSION The ability of DL models to exploit site-specific biases as shortcuts raises concerns about their reliability, generalization, and deployability in clinical settings.
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Affiliation(s)
- Raissa Souza
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Matthias Wilms
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Department of Pediatrics, University of Calgary, Calgary, AB T2N 4N1, Canada
- Department of Community Health Sciences, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Milton Camacho
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - G Bruce Pike
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Richard Camicioli
- Department of Medicine (Neurology), Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB T6G 2E1, Canada
| | - Oury Monchi
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Department of Radiology, Radio-Oncology and Nuclear Medicine, Université de Montréal, Montréal, QC H3C 3J7, Canada
- Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montréal, QC H3W 1W4, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Nils D Forkert
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
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Rojahn J, Palu A, Skiena S, Jones JJ. American public opinion on artificial intelligence in healthcare. PLoS One 2023; 18:e0294028. [PMID: 37943752 PMCID: PMC10635466 DOI: 10.1371/journal.pone.0294028] [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: 01/09/2023] [Accepted: 10/15/2023] [Indexed: 11/12/2023] Open
Abstract
Billions of dollars are being invested into developing medical artificial intelligence (AI) systems and yet public opinion of AI in the medical field seems to be mixed. Although high expectations for the future of medical AI do exist in the American public, anxiety and uncertainty about what it can do and how it works is widespread. Continuing evaluation of public opinion on AI in healthcare is necessary to ensure alignment between patient attitudes and the technologies adopted. We conducted a representative-sample survey (total N = 203) to measure the trust of the American public towards medical AI. Primarily, we contrasted preferences for AI and human professionals to be medical decision-makers. Additionally, we measured expectations for the impact and use of medical AI in the future. We present four noteworthy results: (1) The general public strongly prefers human medical professionals make medical decisions, while at the same time believing they are more likely to make culturally biased decisions than AI. (2) The general public is more comfortable with a human reading their medical records than an AI, both now and "100 years from now." (3) The general public is nearly evenly split between those who would trust their own doctor to use AI and those who would not. (4) Respondents expect AI will improve medical treatment but more so in the distant future than immediately.
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Affiliation(s)
- Jessica Rojahn
- Department of Sociology, Stony Brook University, Stony Brook, New York, United States of America
| | - Andrea Palu
- Department of Sociology, Stony Brook University, Stony Brook, New York, United States of America
| | - Steven Skiena
- Department of Computer Science, Stony Brook University, Stony Brook, New York, United States of America
| | - Jason J. Jones
- Department of Sociology, Stony Brook University, Stony Brook, New York, United States of America
- Institute for Advanced Computational Science, Stony Brook University, Stony Brook, New York, United States of America
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Love CS. "Just the Facts Ma'am": Moral and Ethical Considerations for Artificial Intelligence in Medicine and its Potential to Impact Patient Autonomy and Hope. LINACRE QUARTERLY 2023; 90:375-394. [PMID: 37974568 PMCID: PMC10638968 DOI: 10.1177/00243639231162431] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Applying machine-based learning and synthetic cognition, commonly referred to as artificial intelligence (AI), to medicine intimates prescient knowledge. The ability of these algorithms to potentially unlock secrets held within vast data sets makes them invaluable to healthcare. Complex computer algorithms are routinely used to enhance diagnoses in fields like oncology, cardiology, and neurology. These algorithms have found utility in making healthcare decisions that are often complicated by seemingly endless relationships between exogenous and endogenous variables. They have also found utility in the allocation of limited healthcare resources and the management of end-of-life issues. With the increase in computing power and the ability to test a virtually unlimited number of relationships, scientists and engineers have the unprecedented ability to increase the prognostic confidence that comes from complex data analysis. While these systems present exciting opportunities for the democratization and precision of healthcare, their use raises important moral and ethical considerations around Christian concepts of autonomy and hope. The purpose of this essay is to explore some of the practical limitations associated with AI in medicine and discuss some of the potential theological implications that machine-generated diagnoses may present. Specifically, this article examines how these systems may disrupt the patient and healthcare provider relationship emblematic of Christ's healing mission. Finally, this article seeks to offer insights that might help in the development of a more robust ethical framework for the application of these systems in the future.
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Olawade DB, Wada OJ, David-Olawade AC, Kunonga E, Abaire O, Ling J. Using artificial intelligence to improve public health: a narrative review. Front Public Health 2023; 11:1196397. [PMID: 37954052 PMCID: PMC10637620 DOI: 10.3389/fpubh.2023.1196397] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 09/26/2023] [Indexed: 11/14/2023] Open
Abstract
Artificial intelligence (AI) is a rapidly evolving tool revolutionizing many aspects of healthcare. AI has been predominantly employed in medicine and healthcare administration. However, in public health, the widespread employment of AI only began recently, with the advent of COVID-19. This review examines the advances of AI in public health and the potential challenges that lie ahead. Some of the ways AI has aided public health delivery are via spatial modeling, risk prediction, misinformation control, public health surveillance, disease forecasting, pandemic/epidemic modeling, and health diagnosis. However, the implementation of AI in public health is not universal due to factors including limited infrastructure, lack of technical understanding, data paucity, and ethical/privacy issues.
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Affiliation(s)
- David B. Olawade
- Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, United Kingdom
| | - Ojima J. Wada
- Division of Sustainable Development, Qatar Foundation, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | | | - Edward Kunonga
- School of Health and Life Sciences, Teesside University, Middlesbrough, United Kingdom
| | - Olawale Abaire
- Department of Biochemistry, Adekunle Ajasin University, Akungba-Akoko, Nigeria
| | - Jonathan Ling
- Independent Researcher, Stockton-on-Tees, United Kingdom
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Van Orden K, Meyer DM, Perrinez ES, Torres D, Poynor B, Alwood B, Bykowski J, Khalessi A, Meyer BC. (VISIION-S): Viz.ai Implementation of Stroke augmented Intelligence and communications platform to improve Indicators and Outcomes for a comprehensive stroke center and Network - Sustainability. J Stroke Cerebrovasc Dis 2023; 32:107303. [PMID: 37572556 DOI: 10.1016/j.jstrokecerebrovasdis.2023.107303] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 08/05/2023] [Accepted: 08/07/2023] [Indexed: 08/14/2023] Open
Abstract
OBJECTIVES As Comprehensive Stroke Centers (CSCs) strive to improve neuro-intervention (NIR) times, process improvements are put in place to streamline workflows. Our prior publication (VISIION) demonstrated improvements in key performance indicators (KPIs). The purpose VISIION-S was to analyze whether those results were sustainable. MATERIALS AND METHODS Consecutive Direct Arriving LVO (DALVO) and telemedicine transfer LVO (BEMI) stroke NIR cases were assessed, including subgroups of DALVO-OnHours, DALVO-OffHours, BEMI-OnHours, and BEMI-OffHours. We analyzed times for the original 6 months pre (6/10/20-1/15/21) and compared them to a 17 month post-implementation period (1/16/21- 6/25/22) to evaluate for sustainability. Mann-Whitney U was utilized. RESULTS 150 NIR cases were analyzed pre (n = 47) v. post (n = 103) implementation (DALVO-OnHours 7 v. 20, DALVO-OffHours 10 v. 25, BEMI-OnHours 13 v. 20, BEMI-OffHours 17 v. 38). For Door-to-groin (DTG), improvement was noted for DALVO-OffHours 39%(157 min,96 min;p < 0.001), DALVO-ALL 25%(127 min,95 min;p = 0.006), BEMI-OffHours 46%(45 min,25 min;p = 0.023), and BEMI-ALL 40%(42 min,25 min;p = 0.005). Activation-to-groin (ATG), door-to-device (DTD), and door-to-recanalization (DTR) also showed statistical improvements. For DALVO-OffHours, there were reductions in door to CT (DTC) 80%(26 min,5 min;p < 0.001), ATG 32%(90 min,61 min;p = 0.036), DTG 39%(157 min,96 min;p < 0.001), DTD 31%(178 min,123 min;p = 0.002), and DTR 32%(197 min,135 min;p = 0.003). CONCLUSIONS We noted sustainability over a 17 month period with sustained reduction in KPIs for even more NIR time interval comparisons. In the greatest opportunity subgroup (DALVO-OffHours), we noted a reduction in all 5 time interval metrics. Our sustainability finding is important to show that process improvements continued even after the immediate period, adding credibility to the results. Models such as this could be useful for other centers striving to optimize workflow and improve times.
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Affiliation(s)
- Kim Van Orden
- Department of Neurosciences, University of California, San Diego, California, USA.
| | - Dawn Matherne Meyer
- Department of Neurosciences, University of California, San Diego, California, USA.
| | - Emily S Perrinez
- Department of Neurosciences, University of California, San Diego, California, USA.
| | - Dolores Torres
- Department of Neurosciences, University of California, San Diego, California, USA.
| | - Briana Poynor
- Department of Neurosciences, University of California, San Diego, California, USA.
| | - Ben Alwood
- Department of Neurosciences, University of California, San Diego, California, USA.
| | - Julie Bykowski
- Department of Neurosciences, University of California, San Diego, California, USA.
| | - Alex Khalessi
- Department of Neurosciences, University of California, San Diego, California, USA.
| | - Brett C Meyer
- Department of Neurosciences, University of California, San Diego, California, USA.
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Alaiti RK, Vallio CS, Assunção JH, de Andrade e Silva FB, Gracitelli MEC, Neto AAF, Malavolta EA. Using Machine Learning to Predict Nonachievement of Clinically Significant Outcomes After Rotator Cuff Repair. Orthop J Sports Med 2023; 11:23259671231206180. [PMID: 37868215 PMCID: PMC10588422 DOI: 10.1177/23259671231206180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 05/30/2023] [Indexed: 10/24/2023] Open
Abstract
Background Although some evidence suggests that machine learning algorithms may outperform classical statistical methods in prognosis prediction for several orthopaedic surgeries, to our knowledge, no study has yet used machine learning to predict patient-reported outcome measures after rotator cuff repair. Purpose To determine whether machine learning algorithms using preoperative data can predict the nonachievement of the minimal clinically important difference (MCID) of disability at 2 years after rotator cuff surgical repair with a similar performance to that of other machine learning studies in the orthopaedic surgery literature. Study Design Case-control study; Level of evidence, 3. Methods We evaluated 474 patients (n = 500 shoulders) with rotator cuff tears who underwent arthroscopic rotator cuff repair between January 2013 and April 2019. The study outcome was the difference between the preoperative and 24-month postoperative American Shoulder and Elbow Surgeons (ASES) score. A cutoff score was calculated based on the established MCID of 15.2 points to separate success (higher than the cutoff) from failure (lower than the cutoff). Routinely collected imaging, clinical, and demographic data were used to train 8 machine learning algorithms (random forest classifier; light gradient boosting machine [LightGBM]; decision tree classifier; extra trees classifier; logistic regression; extreme gradient boosting [XGBoost]; k-nearest neighbors [KNN] classifier; and CatBoost classifier). We used a random sample of 70% of patients to train the algorithms, and 30% were left for performance assessment, simulating new data. The performance of the models was evaluated with the area under the receiver operating characteristic curve (AUC). Results The AUCs for all algorithms ranged from 0.58 to 0.68. The random forest classifier and LightGBM presented the highest AUC values (0.68 [95% CI, 0.48-0.79] and 0.67 [95% CI, 0.43-0.75], respectively) of the 8 machine learning algorithms. Most of the machine learning algorithms outperformed logistic regression (AUC, 0.59 [95% CI, 0.48-0.81]); nonetheless, their performance was lower than that of other machine learning studies in the orthopaedic surgery literature. Conclusion Machine learning algorithms demonstrated some ability to predict the nonachievement of the MCID on the ASES 2 years after rotator cuff repair surgery.
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Affiliation(s)
- Rafael Krasic Alaiti
- Research, Technology, and Data Science Office, Grupo Superador, São Paulo, Brazil
- Universidade de São Paulo, São Paulo, Brazil
| | - Caio Sain Vallio
- Health Innovation, Data Science, and MLOps, Semantix, São Paulo, Brazil
| | - Jorge Henrique Assunção
- Faculdade de Medicina, Hospital das Clinicas FMUSP, Universidade de São Paulo, São Paulo, Brazil
- DASA, Hospital 9 de Julho, São Paulo, São Paulo, Brazil
| | | | | | | | - Eduardo Angeli Malavolta
- Faculdade de Medicina, Hospital das Clinicas FMUSP, Universidade de São Paulo, São Paulo, Brazil
- Hospital do Coração, São Paulo, Brazil
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Crilly P. Opportunities and threats for community pharmacy in the era of enhanced technology and artificial intelligence. INTERNATIONAL JOURNAL OF PHARMACY PRACTICE 2023; 31:447-448. [PMID: 37738634 DOI: 10.1093/ijpp/riad065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/24/2023]
Affiliation(s)
- Philip Crilly
- Department of Pharmacy, Faculty of Health, Science, Social Care and Education, Kingston University, Penrhyn Road, Kingston upon Thames, KT1 2EE, United Kingdom
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Cambuli VM, Baroni MG. Intelligent Insulin vs. Artificial Intelligence for Type 1 Diabetes: Will the Real Winner Please Stand Up? Int J Mol Sci 2023; 24:13139. [PMID: 37685946 PMCID: PMC10488097 DOI: 10.3390/ijms241713139] [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: 07/29/2023] [Revised: 08/18/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
Research in the treatment of type 1 diabetes has been addressed into two main areas: the development of "intelligent insulins" capable of auto-regulating their own levels according to glucose concentrations, or the exploitation of artificial intelligence (AI) and its learning capacity, to provide decision support systems to improve automated insulin therapy. This review aims to provide a synthetic overview of the current state of these two research areas, providing an outline of the latest development in the search for "intelligent insulins," and the results of new and promising advances in the use of artificial intelligence to regulate automated insulin infusion and glucose control. The future of insulin treatment in type 1 diabetes appears promising with AI, with research nearly reaching the possibility of finally having a "closed-loop" artificial pancreas.
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Affiliation(s)
- Valentina Maria Cambuli
- Diabetology and Metabolic Diseaseas, San Michele Hospital, ARNAS Giuseppe Brotzu, 09121 Cagliari, Italy;
| | - Marco Giorgio Baroni
- Department of Clinical Medicine, Public Health, Life and Environmental Sciences, University of L’Aquila, 67100 L’Aquila, Italy
- Neuroendocrinology and Metabolic Diseases, IRCCS Neuromed, 86077 Pozzilli, Italy
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Flores E, Salinas JM, Blasco Á, López-Garrigós M, Torreblanca R, Carbonell R, Martínez-Racaj L, Salinas M. Clinical Decision Support systems: A step forward in establishing the clinical laboratory as a decision maker hubA CDS system protocol implementation in the clinical laboratory. Comput Struct Biotechnol J 2023; 22:27-31. [PMID: 37661968 PMCID: PMC10474568 DOI: 10.1016/j.csbj.2023.08.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 07/25/2023] [Accepted: 08/04/2023] [Indexed: 09/05/2023] Open
Abstract
Background New tools for health information technology have been developed in recent times, such as Clinical Decision Support (CDS) systems, which are any digital solutions designed to help healthcare professionals when making clinical decisions. The study aimed to show how we have adopted a CDS system in the San Juan de Alicante Clinical Laboratory and facilitate the implementation of our protocol in other clinical laboratories. We have user experience and the motivation to improve healthcare tools. The improvement, measurement, and monitoring of interventions and laboratory tests has been our motto for years. Materials and methods A descriptive research was conducted. All stages in the design of the project are as follows: 1. Set up a multidisciplinary workgroup. 2. Review patients' data. 3. Identify relevant data from main sources. 4. Design the likely outcomes. 5. Define a complete integration scenario. 6. Monitor and track the impact. To set up this protocol, two new software systems were implemented in our laboratory: AlinIQ CDS v8.2 as Rule Engine, and AlinIQ AIP Integrated Platform v1.6 as Business Intelligence (BI) tool. Results Our protocol shows the workflow and actions that can be done with a CDS system and also how it could be integrated with other monitoring systems, as well as some examples of KPIs and their outcomes. Conclusions CDS could be a great strategic asset for clinical laboratories to improve the integration of care, optimize the use of laboratory tests, and add more clinical value to physicians in the interpretation of results.
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Affiliation(s)
- Emilio Flores
- Clinical Laboratory, University Hospital Sant Joan d'Alacant, Crta. Nacional 332 s/n, 03550, San Juan de Alicante, Spain
- Department of Clinical Medicine, Universidad Miguel Hernández, Crta. Nacional N-332 s/n, 03550, San Juan de Alicante, Spain
| | - José María Salinas
- Informatics Technology and Communication Department, University Hospital Sant Joan d'Alacant, Crta. Nacional 332 s/n, 03550 San Juan de Alicante, Spain
| | - Álvaro Blasco
- Clinical Laboratory, University Hospital Sant Joan d'Alacant, Crta. Nacional 332 s/n, 03550, San Juan de Alicante, Spain
| | - Maite López-Garrigós
- Clinical Laboratory, University Hospital Sant Joan d'Alacant, Crta. Nacional 332 s/n, 03550, San Juan de Alicante, Spain
| | - Ruth Torreblanca
- Clinical Laboratory, University Hospital Sant Joan d'Alacant, Crta. Nacional 332 s/n, 03550, San Juan de Alicante, Spain
| | - Rosa Carbonell
- Clinical Laboratory, University Hospital Sant Joan d'Alacant, Crta. Nacional 332 s/n, 03550, San Juan de Alicante, Spain
| | - Laura Martínez-Racaj
- Clinical Laboratory, University Hospital Sant Joan d'Alacant, Crta. Nacional 332 s/n, 03550, San Juan de Alicante, Spain
- Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO), Av. de Cataluña 21, 46020, Valencia, Spain
| | - Maria Salinas
- Clinical Laboratory, University Hospital Sant Joan d'Alacant, Crta. Nacional 332 s/n, 03550, San Juan de Alicante, Spain
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Tuladhar S, Mwamelo K, Manyama C, Obuobi D, Antunes M, Gashaw M, Vogel M, Shrinivasan H, Mugambwa KA, Korley I, Froeschl G, Hoffaeller L, Scholze S. Proceedings from the CIHLMU 2022 Symposium: "Availability of and Access to Quality Data in Health". BMC Proc 2023; 17:21. [PMID: 37587461 PMCID: PMC10433535 DOI: 10.1186/s12919-023-00270-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/20/2023] [Indexed: 08/18/2023] Open
Abstract
Data is an essential tool for valid and reliable healthcare management. Access to high-quality data is critical to ensuring the early identification of problems, the design of appropriate interventions, and the effective implementation and evaluation of health intervention outcomes. During the COVID-19 pandemic, the need for strong information systems and the value of producing high-quality data for timely response and tracking resources and progress have been very evident across countries. The availability of and access to high-quality data at all levels of the health systems of low and middle-income countries is a challenge, which is exacerbated by multiple parallels and poorly integrated data sources, a lack of data-sharing standards and policy frameworks, their weak enforcement, and inadequate skills among those handling data. Completeness, accuracy, integrity, validity, and timeliness are challenges to data availability and use. "Big Data" is a necessity and a challenge in the current complexities of health systems. In transitioning to digital systems with proper data standards and policy frameworks for privacy protection, data literacy, ownership, and data use at all levels of the health system, skill enhancement of the staff is critical. Adequate funding for strengthening routine information systems and periodic surveys and research, and reciprocal partnerships between high-income countries and low- and middle-income countries in data generation and use, should be prioritized by the low- and middle-income countries to foster evidence-based healthcare practices.
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Affiliation(s)
- Sabita Tuladhar
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany.
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany.
| | - Kimothy Mwamelo
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany
| | - Christina Manyama
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany
| | - Dorothy Obuobi
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany
| | - Mario Antunes
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany
| | - Mulatu Gashaw
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany
| | - Monica Vogel
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany
| | - Harinee Shrinivasan
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany
| | - Kashung Annie Mugambwa
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany
| | - Isabella Korley
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany
| | - Guenter Froeschl
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany
| | - Lisa Hoffaeller
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany
| | - Sarah Scholze
- Teaching & Training Unit, Division of Infectious Diseases and Tropical Medicine, University Hospital, LMU, Munich, Germany
- Center for International Health, Ludwig-Maximilians-Universität, Munich, Germany
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Simon Davis DA, Ritchie M, Hammill D, Garrett J, Slater RO, Otoo N, Orlov A, Gosling K, Price J, Yip D, Jung K, Syed FM, Atmosukarto II, Quah BJC. Identifying cancer-associated leukocyte profiles using high-resolution flow cytometry screening and machine learning. Front Immunol 2023; 14:1211064. [PMID: 37600768 PMCID: PMC10435879 DOI: 10.3389/fimmu.2023.1211064] [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: 04/24/2023] [Accepted: 06/26/2023] [Indexed: 08/22/2023] Open
Abstract
Background Machine learning (ML) is a valuable tool with the potential to aid clinical decision making. Adoption of ML to this end requires data that reliably correlates with the clinical outcome of interest; the advantage of ML is that it can model these correlations from complex multiparameter data sets that can be difficult to interpret conventionally. While currently available clinical data can be used in ML for this purpose, there exists the potential to discover new "biomarkers" that will enhance the effectiveness of ML in clinical decision making. Since the interaction of the immune system and cancer is a hallmark of tumor establishment and progression, one potential area for cancer biomarker discovery is through the investigation of cancer-related immune cell signatures. Hence, we hypothesize that blood immune cell signatures can act as a biomarker for cancer progression. Methods To probe this, we have developed and tested a multiparameter cell-surface marker screening pipeline, using flow cytometry to obtain high-resolution systemic leukocyte population profiles that correlate with detection and characterization of several cancers in murine syngeneic tumor models. Results We discovered a signature of several blood leukocyte subsets, the most notable of which were monocyte subsets, that could be used to train CATboost ML models to predict the presence and type of cancer present in the animals. Conclusions Our findings highlight the potential utility of a screening approach to identify robust leukocyte biomarkers for cancer detection and characterization. This pipeline can easily be adapted to screen for cancer specific leukocyte markers from the blood of cancer patient.
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Affiliation(s)
- David A. Simon Davis
- Irradiation Immunity Interaction Lab, Australian National University, Canberra, ACT, Australia
| | - Melissa Ritchie
- Irradiation Immunity Interaction Lab, Australian National University, Canberra, ACT, Australia
| | - Dillon Hammill
- Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Jessica Garrett
- Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Robert O. Slater
- Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Naomi Otoo
- Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Anna Orlov
- Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Katharine Gosling
- Irradiation Immunity Interaction Lab, Australian National University, Canberra, ACT, Australia
| | - Jason Price
- Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Desmond Yip
- Australian National University, Canberra, ACT, Australia
- Department of Medical Oncology, Canberra Hospital & Health Services, Canberra, ACT, Australia
| | - Kylie Jung
- Irradiation Immunity Interaction Lab, Australian National University, Canberra, ACT, Australia
- Radiation Oncology Department, Canberra Hospital & Health Services, Canberra, ACT, Australia
| | - Farhan M. Syed
- Irradiation Immunity Interaction Lab, Australian National University, Canberra, ACT, Australia
- Radiation Oncology Department, Canberra Hospital & Health Services, Canberra, ACT, Australia
| | - Ines I. Atmosukarto
- Irradiation Immunity Interaction Lab, Australian National University, Canberra, ACT, Australia
- Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Ben J. C. Quah
- Irradiation Immunity Interaction Lab, Australian National University, Canberra, ACT, Australia
- Radiation Oncology Department, Canberra Hospital & Health Services, Canberra, ACT, Australia
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Lukić A, Kudelić N, Antičević V, Lazić-Mosler E, Glunčić V, Hren D, Lukić IK. First-year nursing students' attitudes towards artificial intelligence: Cross-sectional multi-center study. Nurse Educ Pract 2023; 71:103735. [PMID: 37541081 DOI: 10.1016/j.nepr.2023.103735] [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: 03/05/2023] [Revised: 07/25/2023] [Accepted: 07/27/2023] [Indexed: 08/06/2023]
Abstract
AIM To assess the attitudes of nursing students toward artificial intelligence. BACKGROUND Possible applications of artificial intelligence-powered systems in nursing cover all aspects of nursing care, from patient care to risk management. Although the final acceptance of artificial intelligence in practice will depend on positive 'nurses' attitudes toward artificial intelligence, those attitudes have gained little attention so far. DESIGN A cross-sectional multicenter study. METHODS The study was performed at nursing schools of four Croatian universities, surveying a total of 336 first-year nursing students (response rate 69.7%) enrolled in 2021. A validated instrument, the General Attitudes towards Artificial Intelligence Scale, consisting of 20 Likert-type items, was chosen for the study. Where applicable, the items were contextualized for nursing. Four sub-scales were identified based on the outcomes of the factor analysis. RESULTS The average attitude score was (mean ± standard deviation) 64.5 ± 11.7, out of a maximum of 100, which was significantly higher than the neutral score of 60.0 (p < 0.001). The attitude towards AI did not differ across the universities and was not associated with students' age. Male students scored slightly higher than their female colleagues. Scores on subscales "Benefits of artificial intelligence in nursing", "Willingness to use artificial intelligence in nursing practice", and "Dangers of artificial intelligence" were favorable of artificial intelligence-based solutions. However, scores on the subscale "Practical advantages of artificial intelligence" were somewhat unfavorable. CONCLUSIONS First-year nursing students had slightly positive attitudes towards artificial intelligence in nursing, which should make it easier for the new generations of nurses to embrace and implement artificial intelligence systems. Reservations about artificial intelligence in daily nursing practice indicate that nursing students might benefit from education focused specifically on applications of artificial intelligence in nursing.
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Affiliation(s)
- Anita Lukić
- Varaždin General Hospital, Varaždin, Croatia; University of Applied Sciences, Bjelovar, Croatia; University North, Koprivnica, Croatia
| | - Nenad Kudelić
- Varaždin General Hospital, Varaždin, Croatia; University North, Koprivnica, Croatia
| | - Vesna Antičević
- University Department of Health Studies, University of Split, Split, Croatia
| | - Elvira Lazić-Mosler
- Department of Nursing, Catholic University of Croatia, Zagreb, Croatia; School of Medicine, Catholic University of Croatia, Zagreb, Croatia
| | - Vicko Glunčić
- Department of Anesthesiology, Mount Sinai Hospital, Chicago, IL, USA
| | - Darko Hren
- Department of Psychology, Faculty of Humanities and Social Sciences, University of Split, Split, Croatia
| | - Ivan K Lukić
- University of Applied Sciences, Bjelovar, Croatia; School of Medicine, Catholic University of Croatia, Zagreb, Croatia.
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Anastasiadis A, Koudonas A, Langas G, Tsiakaras S, Memmos D, Mykoniatis I, Symeonidis EN, Tsiptsios D, Savvides E, Vakalopoulos I, Dimitriadis G, de la Rosette J. Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review. Asian J Urol 2023; 10:258-274. [PMID: 37538159 PMCID: PMC10394286 DOI: 10.1016/j.ajur.2023.02.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 10/23/2022] [Accepted: 02/10/2023] [Indexed: 08/05/2023] Open
Abstract
Objective To provide a comprehensive review on the existing research and evidence regarding artificial intelligence (AI) applications in the assessment and management of urinary stone disease. Methods A comprehensive literature review was performed using PubMed, Scopus, and Google Scholar databases to identify publications about innovative concepts or supporting applications of AI in the improvement of every medical procedure relating to stone disease. The terms ''endourology'', ''artificial intelligence'', ''machine learning'', and ''urolithiasis'' were used for searching eligible reports, while review articles, articles referring to automated procedures without AI application, and editorial comments were excluded from the final set of publications. The search was conducted from January 2000 to September 2023 and included manuscripts in the English language. Results A total of 69 studies were identified. The main subjects were related to the detection of urinary stones, the prediction of the outcome of conservative or operative management, the optimization of operative procedures, and the elucidation of the relation of urinary stone chemistry with various factors. Conclusion AI represents a useful tool that provides urologists with numerous amenities, which explains the fact that it has gained ground in the pursuit of stone disease management perfection. The effectiveness of diagnosis and therapy can be increased by using it as an alternative or adjunct to the already existing data. However, little is known concerning the potential of this vast field. Electronic patient records, containing big data, offer AI the opportunity to develop and analyze more precise and efficient diagnostic and treatment algorithms. Nevertheless, the existing applications are not generalizable in real-life practice, and high-quality studies are needed to establish the integration of AI in the management of urinary stone disease.
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Affiliation(s)
- Anastasios Anastasiadis
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Antonios Koudonas
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Georgios Langas
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Stavros Tsiakaras
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Dimitrios Memmos
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Ioannis Mykoniatis
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Evangelos N. Symeonidis
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Dimitrios Tsiptsios
- Neurology Department, Democritus University of Thrace, Alexandroupolis, Greece
| | | | - Ioannis Vakalopoulos
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Georgios Dimitriadis
- 1st Department of Urology, Aristotle University of Thessaloniki, School of Medicine, “G.Gennimatas” General Hospital, Thessaloniki, Greece
| | - Jean de la Rosette
- Department of Urology, Istanbul Medipol Mega University Hospital, Istanbul, Turkey
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Gedefaw L, Liu CF, Ip RKL, Tse HF, Yeung MHY, Yip SP, Huang CL. Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders. Cells 2023; 12:1755. [PMID: 37443789 PMCID: PMC10340428 DOI: 10.3390/cells12131755] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 06/21/2023] [Accepted: 06/28/2023] [Indexed: 07/15/2023] Open
Abstract
Artificial intelligence (AI) is a rapidly evolving field of computer science that involves the development of computational programs that can mimic human intelligence. In particular, machine learning and deep learning models have enabled the identification and grouping of patterns within data, leading to the development of AI systems that have been applied in various areas of hematology, including digital pathology, alpha thalassemia patient screening, cytogenetics, immunophenotyping, and sequencing. These AI-assisted methods have shown promise in improving diagnostic accuracy and efficiency, identifying novel biomarkers, and predicting treatment outcomes. However, limitations such as limited databases, lack of validation and standardization, systematic errors, and bias prevent AI from completely replacing manual diagnosis in hematology. In addition, the processing of large amounts of patient data and personal information by AI poses potential data privacy issues, necessitating the development of regulations to evaluate AI systems and address ethical concerns in clinical AI systems. Nonetheless, with continued research and development, AI has the potential to revolutionize the field of hematology and improve patient outcomes. To fully realize this potential, however, the challenges facing AI in hematology must be addressed and overcome.
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Affiliation(s)
- Lealem Gedefaw
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (L.G.); (C.-F.L.); (M.H.Y.Y.)
| | - Chia-Fei Liu
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (L.G.); (C.-F.L.); (M.H.Y.Y.)
| | - Rosalina Ka Ling Ip
- Department of Pathology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China; (R.K.L.I.); (H.-F.T.)
| | - Hing-Fung Tse
- Department of Pathology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China; (R.K.L.I.); (H.-F.T.)
| | - Martin Ho Yin Yeung
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (L.G.); (C.-F.L.); (M.H.Y.Y.)
| | - Shea Ping Yip
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (L.G.); (C.-F.L.); (M.H.Y.Y.)
| | - Chien-Ling Huang
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China; (L.G.); (C.-F.L.); (M.H.Y.Y.)
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Vorisek CN, Stellmach C, Mayer PJ, Klopfenstein SAI, Bures DM, Diehl A, Henningsen M, Ritter K, Thun S. Artificial Intelligence Bias in Health Care: Web-Based Survey. J Med Internet Res 2023; 25:e41089. [PMID: 37347528 PMCID: PMC10337406 DOI: 10.2196/41089] [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: 07/22/2022] [Revised: 11/11/2022] [Accepted: 04/20/2023] [Indexed: 06/23/2023] Open
Abstract
BACKGROUND Resources are increasingly spent on artificial intelligence (AI) solutions for medical applications aiming to improve diagnosis, treatment, and prevention of diseases. While the need for transparency and reduction of bias in data and algorithm development has been addressed in past studies, little is known about the knowledge and perception of bias among AI developers. OBJECTIVE This study's objective was to survey AI specialists in health care to investigate developers' perceptions of bias in AI algorithms for health care applications and their awareness and use of preventative measures. METHODS A web-based survey was provided in both German and English language, comprising a maximum of 41 questions using branching logic within the REDCap web application. Only the results of participants with experience in the field of medical AI applications and complete questionnaires were included for analysis. Demographic data, technical expertise, and perceptions of fairness, as well as knowledge of biases in AI, were analyzed, and variations among gender, age, and work environment were assessed. RESULTS A total of 151 AI specialists completed the web-based survey. The median age was 30 (IQR 26-39) years, and 67% (101/151) of respondents were male. One-third rated their AI development projects as fair (47/151, 31%) or moderately fair (51/151, 34%), 12% (18/151) reported their AI to be barely fair, and 1% (2/151) not fair at all. One participant identifying as diverse rated AI developments as barely fair, and among the 2 undefined gender participants, AI developments were rated as barely fair or moderately fair, respectively. Reasons for biases selected by respondents were lack of fair data (90/132, 68%), guidelines or recommendations (65/132, 49%), or knowledge (60/132, 45%). Half of the respondents worked with image data (83/151, 55%) from 1 center only (76/151, 50%), and 35% (53/151) worked with national data exclusively. CONCLUSIONS This study shows that the perception of biases in AI overall is moderately fair. Gender minorities did not once rate their AI development as fair or very fair. Therefore, further studies need to focus on minorities and women and their perceptions of AI. The results highlight the need to strengthen knowledge about bias in AI and provide guidelines on preventing biases in AI health care applications.
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Affiliation(s)
- Carina Nina Vorisek
- Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Caroline Stellmach
- Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Paula Josephine Mayer
- Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Sophie Anne Ines Klopfenstein
- Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Institute for Medical Informatics, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | | | - Anke Diehl
- Stabsstelle Digitale Transformation, Universitätsmedizin Essen, Essen, Germany
| | - Maike Henningsen
- Faculty of Health, University of Witten/Herdecke, Witten, Germany
| | - Kerstin Ritter
- Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Sylvia Thun
- Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
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Zhang Z, Lu Y, Vosoughi S, Levy J, Christensen B, Salas L. HiTAIC: hierarchical tumor artificial intelligence classifier traces tissue of origin and tumor type in primary and metastasized tumors using DNA methylation. NAR Cancer 2023; 5:zcad017. [PMID: 37089814 PMCID: PMC10113876 DOI: 10.1093/narcan/zcad017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 04/04/2023] [Accepted: 04/13/2023] [Indexed: 04/25/2023] Open
Abstract
Human cancers are heterogenous by their cell composition and origination site. Cancer metastasis generates the conundrum of the unknown origin of migrated tumor cells. Tracing tissue of origin and tumor type in primary and metastasized cancer is vital for clinical significance. DNA methylation alterations play a crucial role in carcinogenesis and mark cell fate differentiation, thus can be used to trace tumor tissue of origin. In this study, we employed a novel tumor-type-specific hierarchical model using genome-scale DNA methylation data to develop a multilayer perceptron model, HiTAIC, to trace tissue of origin and tumor type in 27 cancers from 23 tissue sites in data from 7735 tumors with high resolution, accuracy, and specificity. In tracing primary cancer origin, HiTAIC accuracy was 99% in the test set and 93% in the external validation data set. Metastatic cancers were identified with a 96% accuracy in the external data set. HiTAIC is a user-friendly web-based application through https://sites.dartmouth.edu/salaslabhitaic/. In conclusion, we developed HiTAIC, a DNA methylation-based algorithm, to trace tumor tissue of origin in primary and metastasized cancers. The high accuracy and resolution of tumor tracing using HiTAIC holds promise for clinical assistance in identifying cancer of unknown origin.
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Affiliation(s)
- Ze Zhang
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Quantitative Biomedical Sciences Program, Guarini School of Graduate and Advanced Studies, Dartmouth College, Hanover, NH, USA
| | - Yunrui Lu
- Quantitative Biomedical Sciences Program, Guarini School of Graduate and Advanced Studies, Dartmouth College, Hanover, NH, USA
| | - Soroush Vosoughi
- Department of Computer Science, Dartmouth College, Hanover, NH, USA
| | - Joshua J Levy
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Quantitative Biomedical Sciences Program, Guarini School of Graduate and Advanced Studies, Dartmouth College, Hanover, NH, USA
- Department of Pathology and Dermatology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Brock C Christensen
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
- Quantitative Biomedical Sciences Program, Guarini School of Graduate and Advanced Studies, Dartmouth College, Hanover, NH, USA
- Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
| | - Lucas A Salas
- To whom correspondence should be addressed. Tel: +1 603 646 5420;
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