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Yi RC, Gantz HY, Feldman SR. Utilizing artificial intelligence technology with emotional intelligence in clinical office visits. J DERMATOL TREAT 2024; 35:2374500. [PMID: 39042947 DOI: 10.1080/09546634.2024.2374500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Accepted: 06/04/2024] [Indexed: 07/25/2024]
Affiliation(s)
- Robin C Yi
- Center for Dermatology Research, Department of Dermatology, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - Hannah Y Gantz
- Center for Dermatology Research, Department of Dermatology, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - Steven R Feldman
- Center for Dermatology Research, Department of Dermatology, Wake Forest University School of Medicine, Winston-Salem, North Carolina
- Department of Pathology, Wake Forest University School of Medicine, Winston-Salem, North Carolina
- Department of Social Sciences & Health Policy, Wake Forest University School of Medicine, Winston-Salem, North Carolina
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2
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Knobloch J, Cozart K, Halford Z, Hilaire M, Richter LM, Arnoldi J. Students' perception of the use of artificial intelligence (AI) in pharmacy school. CURRENTS IN PHARMACY TEACHING & LEARNING 2024; 16:102181. [PMID: 39236450 DOI: 10.1016/j.cptl.2024.102181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Revised: 08/06/2024] [Accepted: 08/09/2024] [Indexed: 09/07/2024]
Abstract
INTRODUCTION The increasing adoption of artificial intelligence (AI) among college students, particularly in pharmacy education, raises ethical concerns and prompts debates on responsible usage. The promise of the potential to reduce workload is met with concerns of accuracy issues, algorithmic bias, and the lack of AI education and training. This study aims to understand pharmacy students' perspectives on the use of AI in pharmacy education. METHODS This study used an anonymous 14-question survey distributed among second, third, and fourth-year pharmacy students at four schools of pharmacy in the United States. RESULTS A total of 171 responses were analyzed. Demographic information included institution, class identification (P2, P3, P4), and age range. Regarding the use of AI, 43% of respondents were unaware of limitations of AI tools. Many respondents (45%) had used AI tools to complete assignments, while 42% considered it academic dishonesty. Fifty-six percent believed AI tools could be used ethically. Student perspectives on AI were varied but many expressed that it will be integral to pharmacy education and future practice. CONCLUSIONS This study highlights the nuances of AI usage among pharmacy students. Despite limited education and training on AI, students utilized tools for various tasks. This survey provides evidence that pharmacy students are exploring the use of AI and would likely benefit from education on using AI as a supplement to critical thinking.
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Affiliation(s)
- Joselyn Knobloch
- Southern Illinois University Edwardsville School of Pharmacy, 40 Hairpin Drive, Suite 3204, Campus Box 2000, Edwardsville, IL 62026-2000, United States of America
| | - Kate Cozart
- VA Tennessee Valley Healthcare System, 782 Weatherly Dr., Clarksville, TN 37043, United States of America.
| | - Zachery Halford
- Union University College of Pharmacy, 1050 Union University Dr., Jackson, TN 38305, United States of America.
| | - Michelle Hilaire
- University of Wyoming School of Pharmacy, 1000 E. University Avenue, Laramie, WY 82071, United States of America.
| | - Lisa M Richter
- North Dakota State University School of Pharmacy, Sudro 20A/Dept 2660, PO Box 6050, Fargo, ND 58108-6050, United States of America.
| | - Jennifer Arnoldi
- Clinical Professor of Pharmacy Practice, Southern Illinois University Edwardsville School of Pharmacy, 40 Hairpin Drive, Suite 3204, Campus Box 2000, Edwardsville, IL 62026-2000, United States of America.
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Iftikhar M, Saqib M, Qayyum SN, Asmat R, Mumtaz H, Rehan M, Ullah I, Ud-Din I, Noori S, Khan M, Rehman E, Ejaz Z. Artificial intelligence-driven transformations in diabetes care: a comprehensive literature review. Ann Med Surg (Lond) 2024; 86:5334-5342. [PMID: 39238969 PMCID: PMC11374247 DOI: 10.1097/ms9.0000000000002369] [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: 04/28/2024] [Accepted: 07/05/2024] [Indexed: 09/07/2024] Open
Abstract
Artificial intelligence (AI) has been applied in healthcare for diagnosis, treatments, disease management, and for studying underlying mechanisms and disease complications in diseases like diabetes and metabolic disorders. This review is a comprehensive overview of various applications of AI in the healthcare system for managing diabetes. A literature search was conducted on PubMed to locate studies integrating AI in the diagnosis, treatment, management and prevention of diabetes. As diabetes is now considered a pandemic now so employing AI and machine learning approaches can be applied to limit diabetes in areas with higher prevalence. Machine learning algorithms can visualize big datasets, and make predictions. AI-powered mobile apps and the closed-loop system automated glucose monitoring and insulin delivery can lower the burden on insulin. AI can help identify disease markers and potential risk factors as well. While promising, AI's integration in the medical field is still challenging due to privacy, data security, bias, and transparency. Overall, AI's potential can be harnessed for better patient outcomes through personalized treatment.
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Affiliation(s)
| | | | | | | | | | - Muhammad Rehan
- Al-Nafees Medical College and Hospital, Islamabad, Pakistan
| | | | | | - Samim Noori
- Nangarhar University, Faculty of Medicine, Nangarhar, Afghanistan
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Gurumurthy G, Gurumurthy J, Gurumurthy S. Machine learning in paediatric haematological malignancies: a systematic review of prognosis, toxicity and treatment response models. Pediatr Res 2024:10.1038/s41390-024-03494-9. [PMID: 39215200 DOI: 10.1038/s41390-024-03494-9] [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: 02/17/2024] [Revised: 06/22/2024] [Accepted: 08/05/2024] [Indexed: 09/04/2024]
Abstract
BACKGROUND Machine Learning (ML) has demonstrated potential in enhancing care in adult oncology. However, its application in paediatric haematological malignancies is still emerging, necessitating a comprehensive review of its capabilities and limitations in this area. METHODS A literature search was conducted through Ovid. Studies included focused on ML models in paediatric patients with haematological malignancies. Studies were categorised into thematic groups for analysis. RESULTS Twenty studies, primarily on leukaemia, were included in this review. Studies were organised into thematic categories such as prognoses, treatment responses and toxicity predictions. Prognostic studies showed AUC scores between 0.685 and 0.929, indicating moderate-high predictive accuracy. Treatment response studies demonstrated AUC scores between 0.840 and 0.875, reflecting moderate accuracy. Toxicity prediction studies reported high accuracy with AUC scores from 0.870 to 0.927. Only five studies (25%) performed external validation. Significant heterogeneity was noted in ML tasks, reporting formats, and effect measures across studies, highlighting a lack of standardised reporting and challenges in data comparability. CONCLUSION The clinical applicability of these ML models remains limited by the lack of external validation and methodological heterogeneity. Addressing these challenges through standardised reporting and rigorous external validation is needed to translate ML from a promising research tool into a reliable clinical practice component. IMPACT Key message: Machine Learning (ML) significantly enhances predictive models in paediatric haematological cancers, offering new avenues for personalised treatment strategies. Future research should focus on developing ML models that can integrate with real-time clinical workflows. Addition to literature: Provides a comprehensive overview of current ML applications and trends. It identifies limitations to its applicability, including the limited diversity in datasets, which may affect the generalisability of ML models across different populations. IMPACT Encourages standardisation and external validation in ML studies, aiming to improve patient outcomes through precision medicine in paediatric haematological oncology.
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Affiliation(s)
| | - Juditha Gurumurthy
- School of Cancer and Pharmaceutical Sciences, King's College London, London, UK
| | - Samantha Gurumurthy
- Department of Infectious Diseases & Immunology, Imperial College London, London, UK
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Sallam M, Al-Mahzoum K, Alshuaib O, Alhajri H, Alotaibi F, Alkhurainej D, Al-Balwah MY, Barakat M, Egger J. Language discrepancies in the performance of generative artificial intelligence models: an examination of infectious disease queries in English and Arabic. BMC Infect Dis 2024; 24:799. [PMID: 39118057 PMCID: PMC11308449 DOI: 10.1186/s12879-024-09725-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Accepted: 08/06/2024] [Indexed: 08/10/2024] Open
Abstract
BACKGROUND Assessment of artificial intelligence (AI)-based models across languages is crucial to ensure equitable access and accuracy of information in multilingual contexts. This study aimed to compare AI model efficiency in English and Arabic for infectious disease queries. METHODS The study employed the METRICS checklist for the design and reporting of AI-based studies in healthcare. The AI models tested included ChatGPT-3.5, ChatGPT-4, Bing, and Bard. The queries comprised 15 questions on HIV/AIDS, tuberculosis, malaria, COVID-19, and influenza. The AI-generated content was assessed by two bilingual experts using the validated CLEAR tool. RESULTS In comparing AI models' performance in English and Arabic for infectious disease queries, variability was noted. English queries showed consistently superior performance, with Bard leading, followed by Bing, ChatGPT-4, and ChatGPT-3.5 (P = .012). The same trend was observed in Arabic, albeit without statistical significance (P = .082). Stratified analysis revealed higher scores for English in most CLEAR components, notably in completeness, accuracy, appropriateness, and relevance, especially with ChatGPT-3.5 and Bard. Across the five infectious disease topics, English outperformed Arabic, except for flu queries in Bing and Bard. The four AI models' performance in English was rated as "excellent", significantly outperforming their "above-average" Arabic counterparts (P = .002). CONCLUSIONS Disparity in AI model performance was noticed between English and Arabic in response to infectious disease queries. This language variation can negatively impact the quality of health content delivered by AI models among native speakers of Arabic. This issue is recommended to be addressed by AI developers, with the ultimate goal of enhancing health outcomes.
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Affiliation(s)
- Malik Sallam
- Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, The University of Jordan, Amman, 11942, Jordan.
- Department of Translational Medicine, Faculty of Medicine, Lund University, Malmö, 22184, Sweden.
- Department of Clinical Laboratories and Forensic Medicine, Jordan University Hospital, Queen Rania Al-Abdullah Street-Aljubeiha, P.O. Box: 13046, Amman, Jordan.
| | | | - Omaima Alshuaib
- School of Medicine, The University of Jordan, Amman, 11942, Jordan
| | - Hawajer Alhajri
- School of Medicine, The University of Jordan, Amman, 11942, Jordan
| | - Fatmah Alotaibi
- School of Medicine, The University of Jordan, Amman, 11942, Jordan
| | | | | | - Muna Barakat
- Department of Clinical Pharmacy and Therapeutics, Faculty of Pharmacy, Applied Science Private University, Amman, 11931, Jordan
- MEU Research Unit, Middle East University, Amman, 11831, Jordan
| | - Jan Egger
- Institute for AI in Medicine (IKIM), University Medicine Essen (AöR), Essen, Germany
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Schukow CP, Allen TC. Digital and Computational Pathology Are Pathologists' Physician Extenders. Arch Pathol Lab Med 2024; 148:866-870. [PMID: 38531382 DOI: 10.5858/arpa.2023-0537-ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/11/2024] [Indexed: 03/28/2024]
Affiliation(s)
- Casey P Schukow
- From the Department of Pathology, Corewell Health's Beaumont Hospital, Royal Oak, Michigan
| | - Timothy Craig Allen
- From the Department of Pathology, Corewell Health's Beaumont Hospital, Royal Oak, Michigan
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Agrawal S, Vagha S. A Comprehensive Review of Artificial Intelligence in Prostate Cancer Care: State-of-the-Art Diagnostic Tools and Future Outlook. Cureus 2024; 16:e66225. [PMID: 39238711 PMCID: PMC11374581 DOI: 10.7759/cureus.66225] [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: 07/21/2024] [Accepted: 08/05/2024] [Indexed: 09/07/2024] Open
Abstract
Prostate cancer remains a significant global health challenge, characterized by high incidence and substantial morbidity and mortality rates. Early detection is critical for improving patient outcomes, yet current diagnostic methods have limitations in accuracy and reliability. Artificial intelligence (AI) has emerged as a promising tool to address these challenges in prostate cancer care. AI technologies, including machine learning algorithms and advanced imaging techniques, offer potential solutions to enhance diagnostic accuracy, optimize treatment strategies, and personalize patient care. This review explores the current landscape of AI applications in prostate cancer diagnostics, highlighting state-of-the-art tools and their clinical implications. By synthesizing recent advancements and discussing future directions, the review underscores the transformative potential of AI in revolutionizing prostate cancer diagnosis and management. Ultimately, integrating AI into clinical practice can potentially improve outcomes and quality of life for patients affected by prostate cancer.
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Affiliation(s)
- Somya Agrawal
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Sunita Vagha
- Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Rosselló-Jiménez D, Docampo S, Collado Y, Cuadra-Llopart L, Riba F, Llonch-Masriera M. Geriatrics and artificial intelligence in Spain (Ger-IA project): talking to ChatGPT, a nationwide survey. Eur Geriatr Med 2024; 15:1129-1136. [PMID: 38615289 DOI: 10.1007/s41999-024-00970-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 03/04/2024] [Indexed: 04/15/2024]
Abstract
PURPOSE The purposes of the study was to describe the degree of agreement between geriatricians with the answers given by an AI tool (ChatGPT) in response to questions related to different areas in geriatrics, to study the differences between specialists and residents in geriatrics in terms of the degree of agreement with ChatGPT, and to analyse the mean scores obtained by areas of knowledge/domains. METHODS An observational study was conducted involving 126 doctors from 41 geriatric medicine departments in Spain. Ten questions about geriatric medicine were posed to ChatGPT, and doctors evaluated the AI's answers using a Likert scale. Sociodemographic variables were included. Questions were categorized into five knowledge domains, and means and standard deviations were calculated for each. RESULTS 130 doctors answered the questionnaire. 126 doctors (69.8% women, mean age 41.4 [9.8]) were included in the final analysis. The mean score obtained by ChatGPT was 3.1/5 [0.67]. Specialists rated ChatGPT lower than residents (3.0/5 vs. 3.3/5 points, respectively, P < 0.05). By domains, ChatGPT scored better (M: 3.96; SD: 0.71) in general/theoretical questions rather than in complex decisions/end-of-life situations (M: 2.50; SD: 0.76) and answers related to diagnosis/performing of complementary tests obtained the lowest ones (M: 2.48; SD: 0.77). CONCLUSION Scores presented big variability depending on the area of knowledge. Questions related to theoretical aspects of challenges/future in geriatrics obtained better scores. When it comes to complex decision-making, appropriateness of the therapeutic efforts or decisions about diagnostic tests, professionals indicated a poorer performance. AI is likely to be incorporated into some areas of medicine, but it would still present important limitations, mainly in complex medical decision-making.
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Affiliation(s)
- Daniel Rosselló-Jiménez
- Geriatric Medicine Department, Hospital Universitari de Terrassa, Consorci Sanitari de Terrassa, Carr. Torrebonica, s/n, Terrassa, 08227, Barcelona, Spain.
| | - S Docampo
- Geriatric Medicine Department, Hospital Santa Creu, Tortosa, Tortosa, Tarragona, Spain
| | - Y Collado
- Geriatric Medicine Department, Hospital Universitari de Terrassa, Consorci Sanitari de Terrassa, Carr. Torrebonica, s/n, Terrassa, 08227, Barcelona, Spain
| | - L Cuadra-Llopart
- Geriatric Medicine Department, Hospital Universitari de Terrassa, Consorci Sanitari de Terrassa, Carr. Torrebonica, s/n, Terrassa, 08227, Barcelona, Spain
- Faculty of Medicine and Health Sciences, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
- ACTIUM Functional Anatomy Group, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
| | - F Riba
- Geriatric Medicine Department, Hospital Santa Creu, Tortosa, Tortosa, Tarragona, Spain
| | - M Llonch-Masriera
- Geriatric Medicine Department, Hospital Universitari de Terrassa, Consorci Sanitari de Terrassa, Carr. Torrebonica, s/n, Terrassa, 08227, Barcelona, Spain
- Faculty of Medicine and Health Sciences, Universitat Internacional de Catalunya (UIC), Barcelona, Spain
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Alanzi TM, Alanzi N, Arif WM, Alkhunaifer A, Al Ghaseb L, Albadrani YH, Hasoosah N, Abdullah RA, Al Shullah B. Patient preferences for teleradiology services and remote image interpretation: An empirical study. Nutr Health 2024:2601060241264649. [PMID: 39043374 DOI: 10.1177/02601060241264649] [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: 07/25/2024]
Abstract
STUDY PURPOSE This study aims to examine patient preferences for teleradiology services and remote image interpretation. In this context, this study aims to address the following research questions: (i) How do patients perceive teleradiology services, focusing specifically on comfort, quality of care and satisfaction, communication and information, and accessibility and ease of use; (ii) How do patient demographics (age, gender, urban vs. rural residence) influence the perceptions on teleradiology services? METHODS A cross-sectional survey design is adopted in this study. The survey comprises five sections targeting demographic information, comfort, and preferences regarding remote image interpretation, perceived quality of care and satisfaction, communication and information clarity, and accessibility and technology aspects using five-point Likert scale ratings. A total of 406 patients (209 males and 197 females; 170 urban residents; 174 semi-urban residents, and 62 rural residents) using teleradiology services participated in the study. RESULTS Participants reported high satisfaction with remote image interpretation (3.78 ± 1.19), quality of care (3.31 ± 1.19), understanding (3.84 ± 1.43), and user-friendliness (3.67 ± 1.29). Key issues were technical problems (3.81 ± 1.35), feedback difficulties (3.19 ± 1.58), privacy concerns (2.43 ± 1.46), and low awareness (2.37 ± 1.12). Urban participants scored significantly better in comfort, preferences, and communication than those from semi-urban and rural areas. CONCLUSION Teleradiology design and implementation should be optimized to align with patient preferences and enhance overall satisfaction.
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Affiliation(s)
- Turki M Alanzi
- Public Health College, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Nouf Alanzi
- Department of Clinical Laboratories Sciences, College of Applied Medical Sciences, Jouf University, Jouf, Saudi Arabia
| | - Wejdan M Arif
- Department of Radiological Sciences, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Alhanoof Alkhunaifer
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Lamia Al Ghaseb
- Department of Family Medicine, General Directorate of Health Affairs, Aseer Region, Abha, Saudi Arabia
| | - Yara Hamad Albadrani
- College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia
| | | | - Ruya Adel Abdullah
- College of Medicine, Ibn Sina National College for Medical Studies, Jeddah, Saudi Arabia
| | - Batool Al Shullah
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
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Lee J, Lee JH, Cho K, Park JS. Development of Rapid Disk Diffusion Device Using Laser Speckle Formation Technology for Rapid Antimicrobial Susceptibility Testing. Curr Microbiol 2024; 81:269. [PMID: 39003672 PMCID: PMC11247048 DOI: 10.1007/s00284-024-03798-3] [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: 01/19/2024] [Accepted: 07/05/2024] [Indexed: 07/15/2024]
Abstract
The escalation of antimicrobial resistance (AMR) due to the excessive and inappropriate use of antimicrobials has prompted the urgent need for more rapid and effective antimicrobial susceptibility testing (AST) methods. Conventional AST techniques often take 16-24 h, leading to empirical prescription practices and the potential emergence of AMR. The study aimed to develop a rapid disk diffusion (RDD) method utilizing laser speckle formation (LSF) technology to expedite AST results. The study aimed to evaluate the performance of LSF technology in determining antimicrobial susceptibility. In this study, preclinical and clinical settings were established to compare the LSF technology with conventional disk diffusion (DD) methods to measure the inhibition zones. Preclinical experiments with different bacterial strains demonstrated more than 70% categorical agreement (CA) against most antimicrobials. Further, clinical experiments with multiple strains and antibiotics revealed CA ranging from 40 to 79%, while major and minor discrepancies were observed around 30% and 11%, respectively. These observations revealed high concordance between RDD and DD for multiple antimicrobials in multiple species. The results underscore the potential of RDD-based LSF technology for hastening AST procedures. The current study is marked by a unique equipment setup and analysis approach. Collectively, the suggested laser-based RDD showed greater potential than previously developed comparable methods. The proposed method and design have a higher application potential than formerly developed similar technologies. Together, the study contributes to the ongoing development of rapid AST methods.
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Affiliation(s)
- Jaehyeon Lee
- Department of Laboratory Medicine, Jeonbuk National University Medical School and Hospital, Jeonju, Republic of Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea
| | - Jun Han Lee
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea
| | - Kyoungman Cho
- The Wave Talk., Inc., Jinri Hall, 193, Munji-Ro, Yueseong-Gu, Daejeon, 34051, Republic of Korea.
| | - Jeong Su Park
- Department of Laboratory Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea.
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Abid A, Baxter SL. Breaking Barriers in Behavioral Change: The Potential of Artificial Intelligence-Driven Motivational Interviewing. J Glaucoma 2024; 33:473-477. [PMID: 38595151 DOI: 10.1097/ijg.0000000000002382] [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: 08/01/2023] [Accepted: 03/16/2024] [Indexed: 04/11/2024]
Abstract
Patient outcomes in ophthalmology are greatly influenced by adherence and patient participation, which can be particularly challenging in diseases like glaucoma, where medication regimens can be complex. A well-studied and evidence-based intervention for behavioral change is motivational interviewing (MI), a collaborative and patient-centered counseling approach that has been shown to improve medication adherence in glaucoma patients. However, there are many barriers to clinicians being able to provide motivational interviewing in-office, including short visit durations within high-volume ophthalmology clinics and inadequate billing structures for counseling. Recently, Large Language Models (LLMs), a type of artificial intelligence, have advanced such that they can follow instructions and carry coherent conversations, offering novel solutions to a wide range of clinical problems. In this paper, we discuss the potential of LLMs to provide chatbot-driven MI to improve adherence in glaucoma patients and provide an example conversation as a proof of concept. We discuss the advantages of AI-driven MI, such as demonstrated effectiveness, scalability, and accessibility. We also explore the risks and limitations, including issues of safety and privacy, as well as the factual inaccuracies and hallucinations to which LLMs are susceptible. Domain-specific training may be needed to ensure the accuracy and completeness of information provided in subspecialty areas such as glaucoma. Despite the current limitations, AI-driven motivational interviewing has the potential to offer significant improvements in adherence and should be further explored to maximally leverage the potential of artificial intelligence for our patients.
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Affiliation(s)
- Areeba Abid
- Emory University School of Medicine, Atlanta, GA
| | - Sally L Baxter
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego
- Divison of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA
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Zelenka NR, Di Cara N, Sharma K, Sarvaharman S, Ghataora JS, Parmeggiani F, Nivala J, Abdallah ZS, Marucci L, Gorochowski TE. Data hazards in synthetic biology. Synth Biol (Oxf) 2024; 9:ysae010. [PMID: 38973982 PMCID: PMC11227101 DOI: 10.1093/synbio/ysae010] [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: 03/15/2024] [Revised: 05/17/2024] [Accepted: 06/19/2024] [Indexed: 07/09/2024] Open
Abstract
Data science is playing an increasingly important role in the design and analysis of engineered biology. This has been fueled by the development of high-throughput methods like massively parallel reporter assays, data-rich microscopy techniques, computational protein structure prediction and design, and the development of whole-cell models able to generate huge volumes of data. Although the ability to apply data-centric analyses in these contexts is appealing and increasingly simple to do, it comes with potential risks. For example, how might biases in the underlying data affect the validity of a result and what might the environmental impact of large-scale data analyses be? Here, we present a community-developed framework for assessing data hazards to help address these concerns and demonstrate its application to two synthetic biology case studies. We show the diversity of considerations that arise in common types of bioengineering projects and provide some guidelines and mitigating steps. Understanding potential issues and dangers when working with data and proactively addressing them will be essential for ensuring the appropriate use of emerging data-intensive AI methods and help increase the trustworthiness of their applications in synthetic biology.
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Affiliation(s)
- Natalie R Zelenka
- Jean Golding Institute, University of Bristol, Bristol, UK
- BrisEngBio, University of Bristol, Bristol, UK
| | - Nina Di Cara
- School of Psychological Science, University of Bristol, Bristol, UK
| | - Kieren Sharma
- School of Engineering Mathematics and Technology, University of Bristol, Bristol, UK
| | | | - Jasdeep S Ghataora
- BrisEngBio, University of Bristol, Bristol, UK
- School of Biological Sciences, University of Bristol, Bristol, UK
| | - Fabio Parmeggiani
- BrisEngBio, University of Bristol, Bristol, UK
- School of Biochemistry, University of Bristol, Bristol, UK
- School of Pharmacy and Pharmaceutical Sciences, Cardiff University, Cardiff, UK
| | - Jeff Nivala
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Zahraa S Abdallah
- School of Engineering Mathematics and Technology, University of Bristol, Bristol, UK
| | - Lucia Marucci
- BrisEngBio, University of Bristol, Bristol, UK
- School of Engineering Mathematics and Technology, University of Bristol, Bristol, UK
| | - Thomas E Gorochowski
- BrisEngBio, University of Bristol, Bristol, UK
- School of Biological Sciences, University of Bristol, Bristol, UK
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Ghosh NK, Kumar A. Ultra-minimally invasive endoscopic techniques and colorectal diseases: Current status and its future. Artif Intell Gastrointest Endosc 2024; 5:91424. [DOI: 10.37126/aige.v5.i2.91424] [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: 12/28/2023] [Revised: 04/12/2024] [Accepted: 05/06/2024] [Indexed: 05/11/2024] Open
Abstract
Colorectal diseases are increasing due to altered lifestyle, genetic, and environmental factors. Colonoscopy plays an important role in diagnosis. Advances in colonoscope (ultrathin scope, magnetic scope, capsule) and technological gadgets (Balloon assisted scope, third eye retroscope, NaviAid G-EYE, dye-based chromoendoscopy, virtual chromoendoscopy, narrow band imaging, i-SCAN, etc.) have made colonoscopy more comfortable and efficient. Now in-vivo microscopy can be performed using confocal laser endomicroscopy, optical coherence tomography, spectroscopy, etc. Besides developments in diagnostic colonoscopy, therapeutic colonoscopy has improved to manage lower gastrointestinal tract bleeding, obstruction, perforations, resection polyps, and early colorectal cancers. The introduction of combined endo-laparoscopic surgery and robotic endoscopic surgery has made these interventions feasible. The role of artificial intelligence in the diagnosis and management of colorectal diseases is also increasing day by day. Hence, this article is to review cutting-edge developments in endoscopic principles for the management of colorectal diseases.
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Affiliation(s)
- Nalini Kanta Ghosh
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
| | - Ashok Kumar
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
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14
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Takefuji Y. Generative AI for diabetologists: a concise tutorial on dataset analysis. J Diabetes Metab Disord 2024; 23:1419-1423. [PMID: 38932811 PMCID: PMC11196448 DOI: 10.1007/s40200-023-01377-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 12/18/2023] [Indexed: 06/28/2024]
Abstract
Objectives This paper aims to provide a tutorial for diabetologists and endocrinologists on using generative AI to analyze datasets. It is designed to be accessible to those new to generative AI or without programming experience. Methods The paper presents three examples using a real diabetes dataset. The examples demonstrate binary classification with the 'Group' variable, cross-validation analysis, and NT-proBNP regression. Results The binary classification achieved a prediction accuracy of nearly 0.9. However, the NT-proBNP regression was not successful with this dataset. The calculated R-squared values indicate a poor fit between the predicted model and the raw data. Conclusions The unsuccessful NT-proBNP regression may be due to insufficient training data or the need for additional determinants. The dataset may be too small or new metrics may be required to accurately predict NT-proBNP regression values. It is crucial for users to verify the generated codes to ensure that they can achieve their desired objectives.
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Affiliation(s)
- Yoshiyasu Takefuji
- Faculty of Data Science, Musashino University, 3-3-3 Ariake Koto-Ku, Tokyo, 135-8181 Japan
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15
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Cary MP, De Gagne JC, Kauschinger ED, Carter BM. Advancing Health Equity Through Artificial Intelligence: An Educational Framework for Preparing Nurses in Clinical Practice and Research. Creat Nurs 2024; 30:154-164. [PMID: 38689433 DOI: 10.1177/10784535241249193] [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: 05/02/2024]
Abstract
The integration of artificial intelligence (AI) into health care offers the potential to enhance patient care, improve diagnostic precision, and broaden access to health-care services. Nurses, positioned at the forefront of patient care, play a pivotal role in utilizing AI to foster a more efficient and equitable health-care system. However, to fulfil this role, nurses will require education that prepares them with the necessary skills and knowledge for the effective and ethical application of AI. This article proposes a framework for nurses which includes AI principles, skills, competencies, and curriculum development focused on the practical use of AI, with an emphasis on care that aims to achieve health equity. By adopting this educational framework, nurses will be prepared to make substantial contributions to reducing health disparities and fostering a health-care system that is more efficient and equitable.
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Affiliation(s)
- Michael P Cary
- Duke University School of Nursing, Durham, NC, USA
- Duke University School of Medicine, Durham, NC, USA
- Duke AI Health, Durham, NC, USA
- American Association of Colleges of Nursing, Durham, NC, USA
| | - Jennie C De Gagne
- Duke University School of Nursing, Durham, NC, USA
- Duke University School of Medicine, Durham, NC, USA
- Duke AI Health, Durham, NC, USA
- American Association of Colleges of Nursing, Durham, NC, USA
| | - Elaine D Kauschinger
- Duke University School of Nursing, Durham, NC, USA
- Duke University School of Medicine, Durham, NC, USA
- Duke AI Health, Durham, NC, USA
- American Association of Colleges of Nursing, Durham, NC, USA
| | - Brigit M Carter
- Duke University School of Nursing, Durham, NC, USA
- Duke University School of Medicine, Durham, NC, USA
- Duke AI Health, Durham, NC, USA
- American Association of Colleges of Nursing, Durham, NC, USA
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16
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Hastings N, Samuel D, Ansari AN, Kaurani P, J JW, Bhandary VS, Gautam P, Tayyil Purayil AL, Hassan T, Dinesh Eshwar M, Nuthalapati BST, Pothuri JK, Ali N. The Role of Artificial Intelligence-Powered Imaging in Cerebrovascular Accident Detection. Cureus 2024; 16:e59768. [PMID: 38846243 PMCID: PMC11153838 DOI: 10.7759/cureus.59768] [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: 05/04/2024] [Indexed: 06/09/2024] Open
Abstract
Cerebrovascular accidents (CVAs) often occur suddenly and abruptly, leaving patients with long-lasting disabilities that place a huge emotional and economic burden on everyone involved. CVAs result when emboli or thrombi travel to the brain and impede blood flow; the subsequent lack of oxygen supply leads to ischemia and eventually tissue infarction. The most important factor determining the prognosis of CVA patients is time, specifically the time from the onset of disease to treatment. Artificial intelligence (AI)-assisted neuroimaging alleviates the time constraints of analysis faced using traditional diagnostic imaging modalities, thus shortening the time from diagnosis to treatment. Numerous recent studies support the increased accuracy and processing capabilities of AI-assisted imaging modalities. However, the learning curve is steep, and huge barriers still exist preventing a full-scale implementation of this technology. Thus, the potential for AI to revolutionize medicine and healthcare delivery demands attention. This paper aims to elucidate the progress of AI-powered imaging in CVA diagnosis while considering traditional imaging techniques and suggesting methods to overcome adoption barriers in the hope that AI-assisted neuroimaging will be considered normal practice in the near future. There are multiple modalities for AI neuroimaging, all of which require collecting sufficient data to establish inclusive, accurate, and uniform detection platforms. Future efforts must focus on developing methods for data harmonization and standardization. Furthermore, transparency in the explainability of these technologies needs to be established to facilitate trust between physicians and AI-powered technology. This necessitates considerable resources, both financial and expertise wise which are not available everywhere.
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Affiliation(s)
- Natasha Hastings
- School of Medicine, St. George's University School of Medicine, St. George's, GRD
| | - Dany Samuel
- Radiology, Medical University of Varna, Varna, BGR
| | - Aariz N Ansari
- Internal Medicine, Era's Lucknow Medical College and Hospital, Lucknow, IND
| | - Purvi Kaurani
- Neurology, Dnyandeo Yashwantrao (DY) Patil University School of Medicine, Navi Mumbai, IND
| | - Jenkin Winston J
- Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, IND
| | - Vaibhav S Bhandary
- Radiology, Srinivas Institute of Medical Sciences and Research Center, Mangaluru, IND
| | - Prabin Gautam
- Emergency Medicine, Kettering General Hospital, Kettering, GBR
| | | | - Taimur Hassan
- Neurosurgery, Houston Methodist Neurological Institute, Houston, USA
| | | | | | | | - Noor Ali
- Medicine and Surgery, Dubai Medical College, Dubai, ARE
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17
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Giorgini F, Di Dalmazi G, Diciotti S. Artificial intelligence in endocrinology: a comprehensive review. J Endocrinol Invest 2024; 47:1067-1082. [PMID: 37971630 PMCID: PMC11035463 DOI: 10.1007/s40618-023-02235-9] [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: 07/29/2023] [Accepted: 10/24/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND AND AIM Artificial intelligence (AI) has emerged as a promising technology in the field of endocrinology, offering significant potential to revolutionize the diagnosis, treatment, and management of endocrine disorders. This comprehensive review aims to provide a concise overview of the current landscape of AI applications in endocrinology and metabolism, focusing on the fundamental concepts of AI, including machine learning algorithms and deep learning models. METHODS The review explores various areas of endocrinology where AI has demonstrated its value, encompassing screening and diagnosis, risk prediction, translational research, and "pre-emptive medicine". Within each domain, relevant studies are discussed, offering insights into the methodology and main findings of AI in the treatment of different pathologies, such as diabetes mellitus and related disorders, thyroid disorders, adrenal tumors, and bone and mineral disorders. RESULTS Collectively, these studies show the valuable contributions of AI in optimizing healthcare outcomes and unveiling new understandings of the intricate mechanisms underlying endocrine disorders. Furthermore, AI-driven approaches facilitate the development of precision medicine strategies, enabling tailored interventions for patients based on their individual characteristics and needs. CONCLUSIONS By embracing AI in endocrinology, a future can be envisioned where medical professionals and AI systems synergistically collaborate, ultimately enhancing the lives of individuals affected by endocrine disorders.
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Affiliation(s)
- F Giorgini
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy
- Division of Endocrinology and Diabetes Prevention and Care, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
| | - G Di Dalmazi
- Division of Endocrinology and Diabetes Prevention and Care, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- Department of Medical and Surgical Sciences (DIMEC), Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - S Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy.
- Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, Bologna, Italy.
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18
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Godoy Junior CA, Miele F, Mäkitie L, Fiorenzato E, Koivu M, Bakker LJ, Groot CUD, Redekop WK, van Deen WK. Attitudes Toward the Adoption of Remote Patient Monitoring and Artificial Intelligence in Parkinson's Disease Management: Perspectives of Patients and Neurologists. THE PATIENT 2024; 17:275-285. [PMID: 38182935 DOI: 10.1007/s40271-023-00669-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/10/2023] [Indexed: 01/07/2024]
Abstract
OBJECTIVE Early detection of Parkinson's Disease (PD) progression remains a challenge. As remote patient monitoring solutions (RMS) and artificial intelligence (AI) technologies emerge as potential aids for PD management, there's a gap in understanding how end users view these technologies. This research explores patient and neurologist perspectives on AI-assisted RMS. METHODS Qualitative interviews and focus-groups were conducted with 27 persons with PD (PwPD) and six neurologists from Finland and Italy. The discussions covered traditional disease progression detection and the prospects of integrating AI and RMS. Sessions were recorded, transcribed, and underwent thematic analysis. RESULTS The study involved five individual interviews (four Italian participants and one Finnish) and six focus-groups (four Finnish and two Italian) with PwPD. Additionally, six neurologists (three from each country) were interviewed. Both cohorts voiced frustration with current monitoring methods due to their limited real-time detection capabilities. However, there was enthusiasm for AI-assisted RMS, contingent upon its value addition, user-friendliness, and preservation of the doctor-patient bond. While some PwPD had privacy and trust concerns, the anticipated advantages in symptom regulation seemed to outweigh these apprehensions. DISCUSSION The study reveals a willingness among PwPD and neurologists to integrate RMS and AI into PD management. Widespread adoption requires these technologies to provide tangible clinical benefits, remain user-friendly, and uphold trust within the physician-patient relationship. CONCLUSION This study offers insights into the potential drivers and barriers for adopting AI-assisted RMS in PD care. Recognizing these factors is pivotal for the successful integration of these digital health tools in PD management.
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Affiliation(s)
- Carlos Antonio Godoy Junior
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, Netherlands.
| | - Francesco Miele
- Department of Political and Social Sciences, University of Trieste, Trieste, Italy
| | - Laura Mäkitie
- Department of Neurology, Brain Center, Helsinki University Hospital, Helsinki, Finland
- Department of Clinical Neurosciences, University of Helsinki, Helsinki, Finland
| | | | - Maija Koivu
- Department of Neurology, Brain Center, Helsinki University Hospital, Helsinki, Finland
- Department of Clinical Neurosciences, University of Helsinki, Helsinki, Finland
| | - Lytske Jantien Bakker
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, Netherlands
| | - Carin Uyl-de Groot
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, Netherlands
| | - William Ken Redekop
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, Netherlands
| | - Welmoed Kirsten van Deen
- Erasmus School of Health Policy and Management, Erasmus University Rotterdam, Burgemeester Oudlaan 50, 3062 PA, Rotterdam, Netherlands
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19
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Bsisu I, Alqassieh R, Aloweidi A, Abu-Humdan A, Subuh A, Masarweh D. Attitudes of Jordanian Anesthesiologists and Anesthesia Residents towards Artificial Intelligence: A Cross-Sectional Study. J Pers Med 2024; 14:447. [PMID: 38793029 PMCID: PMC11121815 DOI: 10.3390/jpm14050447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Revised: 03/29/2024] [Accepted: 04/24/2024] [Indexed: 05/26/2024] Open
Abstract
Success in integrating artificial intelligence (AI) in anesthesia depends on collaboration with anesthesiologists, respecting their expertise, and understanding their opinions. The aim of this study was to illustrate the confidence in AI integration in perioperative anesthetic care among Jordanian anesthesiologists and anesthesia residents working at tertiary teaching hospitals. This cross-sectional study was conducted via self-administered online questionnaire and includes 118 responses from 44 anesthesiologists and 74 anesthesia residents. We used a five-point Likert scale to investigate the confidence in AI's role in different aspects of the perioperative period. A significant difference was found between anesthesiologists and anesthesia residents in confidence in the role of AI in operating room logistics and management, with an average score of 3.6 ± 1.3 among residents compared to 2.9 ± 1.4 among specialists (p = 0.012). The role of AI in event prediction under anesthesia scored 3.5 ± 1.4 among residents compared to 2.9 ± 1.4 among specialists (p = 0.032) and the role of AI in decision-making in anesthetic complications 3.3 ± 1.4 among residents and 2.8 ± 1.4 among specialists (p = 0.034). Also, 65 (55.1%) were concerned that the integration of AI will lead to less human-human interaction, while 81 (68.6%) believed that AI-based technology will lead to more adherence to guidelines. In conclusion, AI has the potential to be a revolutionary tool in anesthesia, and hesitancy towards increased dependency on this technology is decreasing with newer generations of practitioners.
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Affiliation(s)
- Isam Bsisu
- Department of Anesthesia and Intensive Care, School of Medicine, The University of Jordan, Amman 11942, Jordan; (A.A.); (A.A.-H.); (D.M.)
- UCSF Center for Health Equity in Surgery and Anesthesia, San Francisco, CA 94158, USA
- Department of Anesthesia and Intensive Care, Arab Medical Center, Amman 11181, Jordan
| | - Rami Alqassieh
- Department of General Surgery and Anesthesia and Urology, Faculty of Medicine, The Hashemite University, Zarqa 13133, Jordan;
| | - Abdelkarim Aloweidi
- Department of Anesthesia and Intensive Care, School of Medicine, The University of Jordan, Amman 11942, Jordan; (A.A.); (A.A.-H.); (D.M.)
| | - Abdulrahman Abu-Humdan
- Department of Anesthesia and Intensive Care, School of Medicine, The University of Jordan, Amman 11942, Jordan; (A.A.); (A.A.-H.); (D.M.)
| | - Aseel Subuh
- Department of Internal Medicine, School of Medicine, The University of Jordan, Amman 11942, Jordan;
| | - Deema Masarweh
- Department of Anesthesia and Intensive Care, School of Medicine, The University of Jordan, Amman 11942, Jordan; (A.A.); (A.A.-H.); (D.M.)
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20
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El-Helaly M. Artificial Intelligence and Occupational Health and Safety, Benefits and Drawbacks. LA MEDICINA DEL LAVORO 2024; 115:e2024014. [PMID: 38686574 PMCID: PMC11181216 DOI: 10.23749/mdl.v115i2.15835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 04/05/2024] [Indexed: 05/02/2024]
Abstract
This paper discusses the impact of artificial intelligence (AI) on occupational health and safety. Although the integration of AI into the field of occupational health and safety is still in its early stages, it has numerous applications in the workplace. Some of these applications offer numerous benefits for the health and safety of workers, such as continuous monitoring of workers' health and safety and the workplace environment through wearable devices and sensors. However, AI might have negative impacts in the workplace, such as ethical worries and data privacy concerns. To maximize the benefits and minimize the drawbacks of AI in the workplace, certain measures should be applied, such as training for both employers and employees and setting policies and guidelines regulating the integration of AI in the workplace.
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Affiliation(s)
- Mohamed El-Helaly
- Occupational and Environmental Medicine, Faculty of Medicine, Mansoura University, Mansoura City, Egypt
- Faculty of Medicine, New Mansoura University, New Mansoura City, Egypt
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21
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Primiero CA, Betz-Stablein B, Ascott N, D’Alessandro B, Gaborit S, Fricker P, Goldsteen A, González-Villà S, Lee K, Nazari S, Nguyen H, Ntouskos V, Pahde F, Pataki BE, Quintana J, Puig S, Rezze GG, Garcia R, Soyer HP, Malvehy J. A protocol for annotation of total body photography for machine learning to analyze skin phenotype and lesion classification. Front Med (Lausanne) 2024; 11:1380984. [PMID: 38654834 PMCID: PMC11035726 DOI: 10.3389/fmed.2024.1380984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 03/25/2024] [Indexed: 04/26/2024] Open
Abstract
Introduction Artificial Intelligence (AI) has proven effective in classifying skin cancers using dermoscopy images. In experimental settings, algorithms have outperformed expert dermatologists in classifying melanoma and keratinocyte cancers. However, clinical application is limited when algorithms are presented with 'untrained' or out-of-distribution lesion categories, often misclassifying benign lesions as malignant, or misclassifying malignant lesions as benign. Another limitation often raised is the lack of clinical context (e.g., medical history) used as input for the AI decision process. The increasing use of Total Body Photography (TBP) in clinical examinations presents new opportunities for AI to perform holistic analysis of the whole patient, rather than a single lesion. Currently there is a lack of existing literature or standards for image annotation of TBP, or on preserving patient privacy during the machine learning process. Methods This protocol describes the methods for the acquisition of patient data, including TBP, medical history, and genetic risk factors, to create a comprehensive dataset for machine learning. 500 patients of various risk profiles will be recruited from two clinical sites (Australia and Spain), to undergo temporal total body imaging, complete surveys on sun behaviors and medical history, and provide a DNA sample. This patient-level metadata is applied to image datasets using DICOM labels. Anonymization and masking methods are applied to preserve patient privacy. A two-step annotation process is followed to label skin images for lesion detection and classification using deep learning models. Skin phenotype characteristics are extracted from images, including innate and facultative skin color, nevi distribution, and UV damage. Several algorithms will be developed relating to skin lesion detection, segmentation and classification, 3D mapping, change detection, and risk profiling. Simultaneously, explainable AI (XAI) methods will be incorporated to foster clinician and patient trust. Additionally, a publicly released dataset of anonymized annotated TBP images will be released for an international challenge to advance the development of new algorithms using this type of data. Conclusion The anticipated results from this protocol are validated AI-based tools to provide holistic risk assessment for individual lesions, and risk stratification of patients to assist clinicians in monitoring for skin cancer.
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Affiliation(s)
- Clare A. Primiero
- Dermatology Department, Hospital Clinic and Fundació Clínic per la Recerca Biomèdica—IDIBAPS, Barcelona, Spain
- Frazer Institute, The University of Queensland, Dermatology Research Centre, Brisbane, QLD, Australia
| | - Brigid Betz-Stablein
- Frazer Institute, The University of Queensland, Dermatology Research Centre, Brisbane, QLD, Australia
| | | | | | | | - Paul Fricker
- Torus Actions & Belle.ai, Ramonville-Saint-Agne, France
| | | | | | - Katie Lee
- Frazer Institute, The University of Queensland, Dermatology Research Centre, Brisbane, QLD, Australia
| | - Sana Nazari
- Computer Vision and Robotics Group, University of Girona, Girona, Spain
| | - Hang Nguyen
- Torus Actions & Belle.ai, Ramonville-Saint-Agne, France
| | - Valsamis Ntouskos
- Remote Sensing Lab, National Technical University of Athens, Athens, Greece
| | | | - Balázs E. Pataki
- HUN-REN Institute for Computer Science and Control, Budapest, Hungary
| | | | - Susana Puig
- Dermatology Department, Hospital Clinic and Fundació Clínic per la Recerca Biomèdica—IDIBAPS, Barcelona, Spain
- Medicine Department, University of Barcelona, Barcelona, Spain
- CIBER de Enfermedades raras, Instituto de Salud Carlos III, Barcelona, Spain
| | - Gisele G. Rezze
- Dermatology Department, Hospital Clinic and Fundació Clínic per la Recerca Biomèdica—IDIBAPS, Barcelona, Spain
| | - Rafael Garcia
- Computer Vision and Robotics Group, University of Girona, Girona, Spain
| | - H. Peter Soyer
- Frazer Institute, The University of Queensland, Dermatology Research Centre, Brisbane, QLD, Australia
- Dermatology Department, Princess Alexandra Hospital, Brisbane, QLD, Australia
| | - Josep Malvehy
- Dermatology Department, Hospital Clinic and Fundació Clínic per la Recerca Biomèdica—IDIBAPS, Barcelona, Spain
- Medicine Department, University of Barcelona, Barcelona, Spain
- CIBER de Enfermedades raras, Instituto de Salud Carlos III, Barcelona, Spain
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22
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Alsanosi SM, Padmanabhan S. Potential Applications of Artificial Intelligence (AI) in Managing Polypharmacy in Saudi Arabia: A Narrative Review. Healthcare (Basel) 2024; 12:788. [PMID: 38610210 PMCID: PMC11011812 DOI: 10.3390/healthcare12070788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 04/03/2024] [Accepted: 04/04/2024] [Indexed: 04/14/2024] Open
Abstract
Prescribing medications is a fundamental practice in the management of illnesses that necessitates in-depth knowledge of clinical pharmacology. Polypharmacy, or the concurrent use of multiple medications by individuals with complex health conditions, poses significant challenges, including an increased risk of drug interactions and adverse reactions. The Saudi Vision 2030 prioritises enhancing healthcare quality and safety, including addressing polypharmacy. Artificial intelligence (AI) offers promising tools to optimise medication plans, predict adverse drug reactions and ensure drug safety. This review explores AI's potential to revolutionise polypharmacy management in Saudi Arabia, highlighting practical applications, challenges and the path forward for the integration of AI solutions into healthcare practices.
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Affiliation(s)
- Safaa M. Alsanosi
- Department of Pharmacology and Toxicology, Faculty of Medicine, Umm Al Qura University, Makkah 24382, Saudi Arabia
- BHF Glasgow Cardiovascular Research Centre, School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow G12 8QQ, UK;
| | - Sandosh Padmanabhan
- BHF Glasgow Cardiovascular Research Centre, School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow G12 8QQ, UK;
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23
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Sarangi PK, Narayan RK, Mohakud S, Vats A, Sahani D, Mondal H. Assessing the Capability of ChatGPT, Google Bard, and Microsoft Bing in Solving Radiology Case Vignettes. Indian J Radiol Imaging 2024; 34:276-282. [PMID: 38549897 PMCID: PMC10972658 DOI: 10.1055/s-0043-1777746] [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] [Indexed: 06/25/2024] Open
Abstract
Background The field of radiology relies on accurate interpretation of medical images for effective diagnosis and patient care. Recent advancements in artificial intelligence (AI) and natural language processing have sparked interest in exploring the potential of AI models in assisting radiologists. However, limited research has been conducted to assess the performance of AI models in radiology case interpretation, particularly in comparison to human experts. Objective This study aimed to evaluate the performance of ChatGPT, Google Bard, and Bing in solving radiology case vignettes (Fellowship of the Royal College of Radiologists 2A [FRCR2A] examination style questions) by comparing their responses to those provided by two radiology residents. Methods A total of 120 multiple-choice questions based on radiology case vignettes were formulated according to the pattern of FRCR2A examination. The questions were presented to ChatGPT, Google Bard, and Bing. Two residents wrote the examination with the same questions in 3 hours. The responses generated by the AI models were collected and compared to the answer keys and explanation of the answers was rated by the two radiologists. A cutoff of 60% was set as the passing score. Results The two residents (63.33 and 57.5%) outperformed the three AI models: Bard (44.17%), Bing (53.33%), and ChatGPT (45%), but only one resident passed the examination. The response patterns among the five respondents were significantly different ( p = 0.0117). In addition, the agreement among the generative AI models was significant (intraclass correlation coefficient [ICC] = 0.628), but there was no agreement between the residents (Kappa = -0.376). The explanation of generative AI models in support of answer was 44.72% accurate. Conclusion Humans exhibited superior accuracy compared to the AI models, showcasing a stronger comprehension of the subject matter. All three AI models included in the study could not achieve the minimum percentage needed to pass an FRCR2A examination. However, generative AI models showed significant agreement in their answers where the residents exhibited low agreement, highlighting a lack of consistency in their responses.
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Affiliation(s)
- Pradosh Kumar Sarangi
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Deoghar, Jharkhand, India
| | - Ravi Kant Narayan
- Department of Anatomy, ESIC Medical College & Hospital, Bihta, Patna, Bihar, India
| | - Sudipta Mohakud
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Aditi Vats
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Debabrata Sahani
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Bhubaneswar, Odisha, India
| | - Himel Mondal
- Department of Physiology, All India Institute of Medical Sciences, Deoghar, Jharkhand, India
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24
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Lakkimsetti M, Devella SG, Patel KB, Dhandibhotla S, Kaur J, Mathew M, Kataria J, Nallani M, Farwa UE, Patel T, Egbujo UC, Meenashi Sundaram D, Kenawy S, Roy M, Khan SF. Optimizing the Clinical Direction of Artificial Intelligence With Health Policy: A Narrative Review of the Literature. Cureus 2024; 16:e58400. [PMID: 38756258 PMCID: PMC11098056 DOI: 10.7759/cureus.58400] [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: 04/16/2024] [Indexed: 05/18/2024] Open
Abstract
Artificial intelligence (AI) has the ability to completely transform the healthcare industry by enhancing diagnosis, treatment, and resource allocation. To ensure patient safety and equitable access to healthcare, it also presents ethical and practical issues that need to be carefully addressed. Its integration into healthcare is a crucial topic. To realize its full potential, however, the ethical issues around data privacy, prejudice, and transparency, as well as the practical difficulties posed by workforce adaptability and statutory frameworks, must be addressed. While there is growing knowledge about the advantages of AI in healthcare, there is a significant lack of knowledge about the moral and practical issues that come with its application, particularly in the setting of emergency and critical care. The majority of current research tends to concentrate on the benefits of AI, but thorough studies that investigate the potential disadvantages and ethical issues are scarce. The purpose of our article is to identify and examine the ethical and practical difficulties that arise when implementing AI in emergency medicine and critical care, to provide solutions to these issues, and to give suggestions to healthcare professionals and policymakers. In order to responsibly and successfully integrate AI in these important healthcare domains, policymakers and healthcare professionals must collaborate to create strong regulatory frameworks, safeguard data privacy, remove prejudice, and give healthcare workers the necessary training.
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Affiliation(s)
| | - Swati G Devella
- Medicine, Kempegowda Institute of Medical Sciences, Bangalore, IND
| | - Keval B Patel
- Surgery, Narendra Modi Medical College, Ahmedabad, IND
| | | | | | - Midhun Mathew
- Internal Medicine, Trinitas Regional Medical Center, Elizabeth, USA
| | | | - Manisha Nallani
- Medicine, Kamineni Academy of Medical Sciences and Research Center, Hyderabad, IND
| | - Umm E Farwa
- Emergency Medicine, Jinnah Sindh Medical University, Karachi, PAK
| | - Tirath Patel
- Medicine, American University of Antigua, Saint John's, ATG
| | | | - Dakshin Meenashi Sundaram
- Internal Medicine, Employees' State Insurance Corporation (ESIC) Medical College & Post Graduate Institute of Medical Science and Research (PGIMSR), Chennai, IND
| | | | - Mehak Roy
- Internal Medicine, School of Medicine Science and Research, Delhi, IND
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Al Hendi KD, Alyami MH, Alkahtany M, Dwivedi A, Alsaqour HG. Artificial intelligence in prosthodontics. Bioinformation 2024; 20:238-242. [PMID: 38712003 PMCID: PMC11069608 DOI: 10.6026/973206300200238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 03/31/2024] [Accepted: 03/31/2024] [Indexed: 05/08/2024] Open
Abstract
Artificial Intelligence (AI) is gaining popularity worldwide owing to its significant impact in science and innovation. The role of AI in prosthodontics has increased significantly in recent years. AI is used for diagnosis, decision-making, prognosis, treatment planning and prediction of outcomes. Integration of AI into prosthodontics can enhance the accuracy and precision of dental practice. However, limited datasets are a major constraint in its practical applications.
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Affiliation(s)
| | | | - Mashary Alkahtany
- Prosthetic Dental Sciences, Faculty of Dentistry, Najran University, Najran, KSA
| | - Alok Dwivedi
- Prosthetic Dental Sciences, Faculty of Dentistry, Najran University, Najran, KSA
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Caglayan A, Slusarczyk W, Rabbani RD, Ghose A, Papadopoulos V, Boussios S. Large Language Models in Oncology: Revolution or Cause for Concern? Curr Oncol 2024; 31:1817-1830. [PMID: 38668040 PMCID: PMC11049602 DOI: 10.3390/curroncol31040137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 03/13/2024] [Accepted: 03/29/2024] [Indexed: 04/28/2024] Open
Abstract
The technological capability of artificial intelligence (AI) continues to advance with great strength. Recently, the release of large language models has taken the world by storm with concurrent excitement and concern. As a consequence of their impressive ability and versatility, their provide a potential opportunity for implementation in oncology. Areas of possible application include supporting clinical decision making, education, and contributing to cancer research. Despite the promises that these novel systems can offer, several limitations and barriers challenge their implementation. It is imperative that concerns, such as accountability, data inaccuracy, and data protection, are addressed prior to their integration in oncology. As the progression of artificial intelligence systems continues, new ethical and practical dilemmas will also be approached; thus, the evaluation of these limitations and concerns will be dynamic in nature. This review offers a comprehensive overview of the potential application of large language models in oncology, as well as concerns surrounding their implementation in cancer care.
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Affiliation(s)
- Aydin Caglayan
- Department of Medical Oncology, Medway NHS Foundation Trust, Gillingham ME7 5NY, UK; (A.C.); (R.D.R.); (A.G.)
| | | | - Rukhshana Dina Rabbani
- Department of Medical Oncology, Medway NHS Foundation Trust, Gillingham ME7 5NY, UK; (A.C.); (R.D.R.); (A.G.)
| | - Aruni Ghose
- Department of Medical Oncology, Medway NHS Foundation Trust, Gillingham ME7 5NY, UK; (A.C.); (R.D.R.); (A.G.)
- Department of Medical Oncology, Barts Cancer Centre, St Bartholomew’s Hospital, Barts Heath NHS Trust, London EC1A 7BE, UK
- Department of Medical Oncology, Mount Vernon Cancer Centre, East and North Hertfordshire Trust, London HA6 2RN, UK
- Health Systems and Treatment Optimisation Network, European Cancer Organisation, 1040 Brussels, Belgium
- Oncology Council, Royal Society of Medicine, London W1G 0AE, UK
| | | | - Stergios Boussios
- Department of Medical Oncology, Medway NHS Foundation Trust, Gillingham ME7 5NY, UK; (A.C.); (R.D.R.); (A.G.)
- Kent Medway Medical School, University of Kent, Canterbury CT2 7LX, UK;
- Faculty of Life Sciences & Medicine, School of Cancer & Pharmaceutical Sciences, King’s College London, Strand Campus, London WC2R 2LS, UK
- Faculty of Medicine, Health, and Social Care, Canterbury Christ Church University, Canterbury CT2 7PB, UK
- AELIA Organization, 9th Km Thessaloniki—Thermi, 57001 Thessaloniki, Greece
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Erden Y, Temel MH, Bağcıer F. Artificial intelligence insights into osteoporosis: assessing ChatGPT's information quality and readability. Arch Osteoporos 2024; 19:17. [PMID: 38499716 DOI: 10.1007/s11657-024-01376-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 03/07/2024] [Indexed: 03/20/2024]
Abstract
Accessible, accurate information, and readability play crucial role in empowering individuals managing osteoporosis. This study showed that the responses generated by ChatGPT regarding osteoporosis had serious problems with quality and were at a level of complexity that that necessitates an educational background of approximately 17 years. PURPOSE The use of artificial intelligence (AI) applications as a source of information in the field of health is increasing. Readable and accurate information plays a critical role in empowering patients to make decisions about their disease. The aim was to examine the quality and readability of responses provided by ChatGPT, an AI chatbot, to commonly asked questions regarding osteoporosis, representing a major public health problem. METHODS "Osteoporosis," "female osteoporosis," and "male osteoporosis" were identified by using Google trends for the 25 most frequently searched keywords on Google. A selected set of 38 keywords was sequentially inputted into the chat interface of the ChatGPT. The responses were evaluated with tools of the Ensuring Quality Information for Patients (EQIP), the Flesch-Kincaid Grade Level (FKGL), and the Flesch-Kincaid Reading Ease (FKRE). RESULTS The EQIP score of the texts ranged from a minimum of 36.36 to a maximum of 61.76 with a mean value of 48.71 as having "serious problems with quality." The FKRE scores spanned from 13.71 to 56.06 with a mean value of 28.71 and the FKGL varied between 8.48 and 17.63, with a mean value of 13.25. There were no statistically significant correlations between the EQIP score and the FKGL or FKRE scores. CONCLUSIONS Although ChatGPT is easily accessible for patients to obtain information about osteoporosis, its current quality and readability fall short of meeting comprehensive healthcare standards.
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Affiliation(s)
- Yakup Erden
- Clinic of Physical Medicine and Rehabilitation, İzzet Baysal Physical Treatment and Rehabilitation Training and Research Hospital, Orüs Street, No. 59, 14020, Bolu, Turkey.
| | - Mustafa Hüseyin Temel
- Department of Physical Medicine and Rehabilitation, Üsküdar State Hospital, Barbaros, Veysi Paşa Street, No. 14, 34662, Istanbul, Turkey
| | - Fatih Bağcıer
- Clinic of Physical Medicine and Rehabilitation, Başakşehir Çam and Sakura City Hospital, Olympic Boulevard Road, 34480, Istanbul, Turkey
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Gala D, Behl H, Shah M, Makaryus AN. The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature. Healthcare (Basel) 2024; 12:481. [PMID: 38391856 PMCID: PMC10887513 DOI: 10.3390/healthcare12040481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 02/13/2024] [Accepted: 02/14/2024] [Indexed: 02/24/2024] Open
Abstract
Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.
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Affiliation(s)
- Dhir Gala
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Haditya Behl
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Mili Shah
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Amgad N Makaryus
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra University, 500 Hofstra Blvd., Hempstead, NY 11549, USA
- Department of Cardiology, Nassau University Medical Center, Hempstead, NY 11554, USA
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Coghlan S, Gyngell C, Vears DF. Ethics of artificial intelligence in prenatal and pediatric genomic medicine. J Community Genet 2024; 15:13-24. [PMID: 37796364 PMCID: PMC10857992 DOI: 10.1007/s12687-023-00678-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 09/27/2023] [Indexed: 10/06/2023] Open
Abstract
This paper examines the ethics of introducing emerging forms of artificial intelligence (AI) into prenatal and pediatric genomic medicine. Application of genomic AI to these early life settings has not received much attention in the ethics literature. We focus on three contexts: (1) prenatal genomic sequencing for possible fetal abnormalities, (2) rapid genomic sequencing for critically ill children, and (3) reanalysis of genomic data obtained from children for diagnostic purposes. The paper identifies and discusses various ethical issues in the possible application of genomic AI in these settings, especially as they relate to concepts of beneficence, nonmaleficence, respect for autonomy, justice, transparency, accountability, privacy, and trust. The examination will inform the ethically sound introduction of genomic AI in early human life.
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Affiliation(s)
- Simon Coghlan
- School of Computing and Information Systems (CIS), Centre for AI and Digital Ethics (CAIDE), The University of Melbourne, Grattan St, Melbourne, Victoria, 3010, Australia.
- Australian Research Council Centre of Excellence for Automated Decision Making and Society (ADM+S), Melbourne, Victoria, Australia.
| | - Christopher Gyngell
- Biomedical Ethics Research Group, Murdoch Children's Research Institute, The Royal Children's Hospital, 50 Flemington Rd, Parkville, Victoria, 3052, Australia
- University of Melbourne, Parkville, Victoria, 3052, Australia
| | - Danya F Vears
- Biomedical Ethics Research Group, Murdoch Children's Research Institute, The Royal Children's Hospital, 50 Flemington Rd, Parkville, Victoria, 3052, Australia
- University of Melbourne, Parkville, Victoria, 3052, Australia
- Centre for Biomedical Ethics and Law, KU Leuven, Kapucijnenvoer 35, 3000, Leuven, Belgium
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Almoammar KA. Harnessing the Power of Artificial Intelligence in Cleft Lip and Palate: An In-Depth Analysis from Diagnosis to Treatment, a Comprehensive Review. CHILDREN (BASEL, SWITZERLAND) 2024; 11:140. [PMID: 38397252 PMCID: PMC10886996 DOI: 10.3390/children11020140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 01/09/2024] [Accepted: 01/14/2024] [Indexed: 02/25/2024]
Abstract
Cleft lip and palate (CLP) is the most common craniofacial malformation, with a range of physical, psychological, and aesthetic consequences. In this comprehensive review, our main objective is to thoroughly examine the relationship between CLP anomalies and the use of artificial intelligence (AI) in children. Additionally, we aim to explore how the integration of AI technology can bring about significant advancements in the fields of diagnosis, treatment methods, and predictive outcomes. By analyzing the existing evidence, we will highlight state-of-the-art algorithms and predictive AI models that play a crucial role in achieving precise diagnosis, susceptibility assessment, and treatment planning for children with CLP anomalies. Our focus will specifically be on the efficacy of alveolar bone graft and orthodontic interventions. The findings of this review showed that deep learning (DL) models revolutionize the diagnostic process, predict susceptibility to CLP, and enhance alveolar bone grafts and orthodontic treatment. DL models surpass human capabilities in terms of precision, and AI algorithms applied to large datasets can uncover the intricate genetic and environmental factors contributing to CLP. Additionally, Machine learning aids in preoperative planning for alveolar bone grafts and provides personalized treatment plans in orthodontic treatment. In conclusion, these advancements inspire optimism for a future where AI seamlessly integrates with CLP management, augmenting its analytical capabilities.
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Affiliation(s)
- Khalid A Almoammar
- Department of Pediatric Dentistry and Orthodontics, College of Dentistry, King Saud University, P.O. Box 60169, Riyadh 11545, Saudi Arabia
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Zaidi SF, Shaikh A, Surani S. The Pulse of AI: Implementation of Artificial Intelligence in Healthcare and its Potential Hazards. Open Respir Med J 2024; 18:e18743064289936. [PMID: 38660683 PMCID: PMC11037519 DOI: 10.2174/0118743064289936240115105057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 12/29/2023] [Accepted: 01/09/2024] [Indexed: 04/26/2024] Open
Abstract
In this editorial, we explore the existing utilization of artificial intelligence (AI) within the healthcare industry, examining both its scope and potential harms if implemented and relied upon on a broader scale. Collaboration among corporations, government bodies, policymakers, and medical experts is essential to address potential concerns, ensuring smooth AI integration into healthcare systems.
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Affiliation(s)
| | - Asim Shaikh
- Department of Medicine, The Aga Khan University, Karachi74800, Pakistan
| | - Salim Surani
- Department of Medicine & Pharmacology, Texas A & M University, College Station, Texas77840, USA
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Youssef Y, De Wet D, Back DA, Scherer J. Digitalization in orthopaedics: a narrative review. Front Surg 2024; 10:1325423. [PMID: 38274350 PMCID: PMC10808497 DOI: 10.3389/fsurg.2023.1325423] [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: 10/21/2023] [Accepted: 12/27/2023] [Indexed: 01/27/2024] Open
Abstract
Advances in technology and digital tools like the Internet of Things (IoT), artificial intelligence (AI), and sensors are shaping the field of orthopaedic surgery on all levels, from patient care to research and facilitation of logistic processes. Especially the COVID-19 pandemic, with the associated contact restrictions was an accelerator for the development and introduction of telemedical applications and digital alternatives to classical in-person patient care. Digital applications already used in orthopaedic surgery include telemedical support, online video consultations, monitoring of patients using wearables, smart devices, surgical navigation, robotic-assisted surgery, and applications of artificial intelligence in forms of medical image processing, three-dimensional (3D)-modelling, and simulations. In addition to that immersive technologies like virtual, augmented, and mixed reality are increasingly used in training but also rehabilitative and surgical settings. Digital advances can therefore increase the accessibility, efficiency and capabilities of orthopaedic services and facilitate more data-driven, personalized patient care, strengthening the self-responsibility of patients and supporting interdisciplinary healthcare providers to offer for the optimal care for their patients.
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Affiliation(s)
- Yasmin Youssef
- Department of Orthopaedics, Trauma and Plastic Surgery, University Hospital of Leipzig, Leipzig, Germany
| | - Deana De Wet
- Orthopaedic Research Unit, University of Cape Town, Cape Town, South Africa
| | - David A. Back
- Center for Musculoskeletal Surgery, Charité University Medicine Berlin, Berlin, Germany
| | - Julian Scherer
- Orthopaedic Research Unit, University of Cape Town, Cape Town, South Africa
- Department of Traumatology, University Hospital of Zurich, Zurich, Switzerland
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Mori R, Okawa M, Tokumaru Y, Niwa Y, Matsuhashi N, Futamura M. Application of an artificial intelligence-based system in the diagnosis of breast ultrasound images obtained using a smartphone. World J Surg Oncol 2024; 22:2. [PMID: 38167161 PMCID: PMC10759682 DOI: 10.1186/s12957-023-03286-1] [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: 10/02/2023] [Accepted: 12/26/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Breast ultrasound (US) is useful for dense breasts, and the introduction of artificial intelligence (AI)-assisted diagnoses of breast US images should be considered. However, the implementation of AI-based technologies in clinical practice is problematic because of the costs of introducing such approaches to hospital information systems (HISs) and the security risk of connecting HIS to the Internet to access AI services. To solve these problems, we developed a system that applies AI to the analysis of breast US images captured using a smartphone. METHODS Training data were prepared using 115 images of benign lesions and 201 images of malignant lesions acquired at the Division of Breast Surgery, Gifu University Hospital. YOLOv3 (object detection models) was used to detect lesions on US images. A graphical user interface (GUI) was developed to predict an AI server. A smartphone application was also developed for capturing US images displayed on the HIS monitor with its camera and displaying the prediction results received from the AI server. The sensitivity and specificity of the prediction performed on the AI server and via the smartphone were calculated using 60 images spared from the training. RESULTS The established AI showed 100% sensitivity and 75% specificity for malignant lesions and took 0.2 s per prediction with the AI sever. Prediction using a smartphone required 2 s per prediction and showed 100% sensitivity and 97.5% specificity for malignant lesions. CONCLUSIONS Good-quality predictions were obtained using the AI server. Moreover, the quality of the prediction via the smartphone was slightly better than that on the AI server, which can be safely and inexpensively introduced into HISs.
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Affiliation(s)
- Ryutaro Mori
- Department of Breast Surgery, Gifu University Hospital, 1-1 Yanagido, Gifu, 501-1194, Japan.
| | - Mai Okawa
- Department of Breast Surgery, Gifu University Hospital, 1-1 Yanagido, Gifu, 501-1194, Japan
| | - Yoshihisa Tokumaru
- Department of Breast Surgery, Gifu University Hospital, 1-1 Yanagido, Gifu, 501-1194, Japan
| | - Yoshimi Niwa
- Department of Breast Surgery, Gifu University Hospital, 1-1 Yanagido, Gifu, 501-1194, Japan
| | - Nobuhisa Matsuhashi
- Department of Gastroenterological Surgery Pediatric Surgery, Gifu University, Graduate School of Medicine, 1-1 Yanagido, Gifu, 501-1194, Japan
| | - Manabu Futamura
- Department of Breast Surgery, Gifu University Hospital, 1-1 Yanagido, Gifu, 501-1194, Japan
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Rahman MA, Victoros E, Ernest J, Davis R, Shanjana Y, Islam MR. Impact of Artificial Intelligence (AI) Technology in Healthcare Sector: A Critical Evaluation of Both Sides of the Coin. CLINICAL PATHOLOGY (THOUSAND OAKS, VENTURA COUNTY, CALIF.) 2024; 17:2632010X241226887. [PMID: 38264676 PMCID: PMC10804900 DOI: 10.1177/2632010x241226887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 12/27/2023] [Indexed: 01/25/2024]
Abstract
The influence of artificial intelligence (AI) has drastically risen in recent years, especially in the field of medicine. Its influence has spread so greatly that it is determined to become a pillar in the future medical world. A comprehensive literature search related to AI in healthcare was performed in the PubMed database and retrieved the relevant information from suitable ones. AI excels in aspects such as rapid adaptation, high diagnostic accuracy, and data management that can help improve workforce productivity. With this potential in sight, the FDA has continuously approved more machine learning (ML) software to be used by medical workers and scientists. However, there are few controversies such as increased chances of data breaches, concern for clinical implementation, and potential healthcare dilemmas. In this article, the positive and negative aspects of AI implementation in healthcare are discussed, as well as recommended some potential solutions to the potential issues at hand.
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Affiliation(s)
| | | | - Julianne Ernest
- Nesbitt School of Pharmacy Wilkes University, Wilkes-Barre, PA, USA
| | - Rob Davis
- Nesbitt School of Pharmacy Wilkes University, Wilkes-Barre, PA, USA
| | - Yeasna Shanjana
- Department of Environmental Sciences, North South University, Bashundhara, Dhaka, Bangladesh
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Qin R, Jin T, Xu F. Biomarkers predicting the efficacy of immune checkpoint inhibitors in hepatocellular carcinoma. Front Immunol 2023; 14:1326097. [PMID: 38187399 PMCID: PMC10770866 DOI: 10.3389/fimmu.2023.1326097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Accepted: 12/11/2023] [Indexed: 01/09/2024] Open
Abstract
In recent years, immune checkpoint inhibitors (ICIs) have emerged as a transformative approach in treating advanced hepatocellular carcinoma (HCC). Despite their success, challenges persist, including concerns about their effectiveness, treatment costs, frequent occurrence of treatment-related adverse events, and tumor hyperprogression. Therefore, it is imperative to identify indicators capable of predicting the efficacy of ICIs treatment, enabling optimal patient selection to maximize clinical benefits while minimizing unnecessary toxic side effects and economic losses. This review paper categorizes prognostic biomarkers of ICIs treatment into the following categories: biochemical and cytological indicators, tumor-related markers, imaging and personal features, etiology, gut microbiome, and immune-related adverse events (irAEs). By organizing these indicators systematically, we aim to guide biomarker exploration and inform clinical treatment decisions.
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Affiliation(s)
| | - Tianqiang Jin
- Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, China
| | - Feng Xu
- Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, China
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Al-Tohamy A, Grove A. Targeting bacterial transcription factors for infection control: opportunities and challenges. Transcription 2023:1-28. [PMID: 38126125 DOI: 10.1080/21541264.2023.2293523] [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: 08/31/2023] [Accepted: 12/07/2023] [Indexed: 12/23/2023] Open
Abstract
The rising threat of antibiotic resistance in pathogenic bacteria emphasizes the need for new therapeutic strategies. This review focuses on bacterial transcription factors (TFs), which play crucial roles in bacterial pathogenesis. We discuss the regulatory roles of these factors through examples, and we outline potential therapeutic strategies targeting bacterial TFs. Specifically, we discuss the use of small molecules to interfere with TF function and the development of transcription factor decoys, oligonucleotides that compete with promoters for TF binding. We also cover peptides that target the interaction between the bacterial TF and other factors, such as RNA polymerase, and the targeting of sigma factors. These strategies, while promising, come with challenges, from identifying targets to designing interventions, managing side effects, and accounting for changing bacterial resistance patterns. We also delve into how Artificial Intelligence contributes to these efforts and how it may be exploited in the future, and we touch on the roles of multidisciplinary collaboration and policy to advance this research domain.Abbreviations: AI, artificial intelligence; CNN, convolutional neural networks; DTI: drug-target interaction; HTH, helix-turn-helix; IHF, integration host factor; LTTRs, LysR-type transcriptional regulators; MarR, multiple antibiotic resistance regulator; MRSA, methicillin resistant Staphylococcus aureus; MSA: multiple sequence alignment; NAP, nucleoid-associated protein; PROTACs, proteolysis targeting chimeras; RNAP, RNA polymerase; TF, transcription factor; TFD, transcription factor decoying; TFTRs, TetR-family transcriptional regulators; wHTH, winged helix-turn-helix.
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Affiliation(s)
- Ahmed Al-Tohamy
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA
- Department of Cell Biology, Biotechnology Research Institute, National Research Centre, Cairo, Egypt
| | - Anne Grove
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA
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Mosleh R, Jarrar Q, Jarrar Y, Tazkarji M, Hawash M. Medicine and Pharmacy Students' Knowledge, Attitudes, and Practice regarding Artificial Intelligence Programs: Jordan and West Bank of Palestine. ADVANCES IN MEDICAL EDUCATION AND PRACTICE 2023; 14:1391-1400. [PMID: 38106923 PMCID: PMC10721701 DOI: 10.2147/amep.s433255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 11/04/2023] [Indexed: 12/19/2023]
Abstract
Background Artificial intelligence (AI) programs generate responses to input text, showcasing their innovative capabilities in education and demonstrating various potential benefits, particularly in the field of medical education. The current knowledge of health profession students about AI programs has still not been assessed in Jordan and the West Bank of Palestine (WBP). Aim This study aimed to assess students' awareness and practice of AI programs in medicine and pharmacy in Jordan and the WBP. Methods This study was in the form of an observational, cross-sectional survey. A questionnaire was electronically distributed among students of medicine and pharmacy at An-Najah National University (WBP), Al-Isra University (Jordan), and Al-Balqa Applied University (Jordan). The questionnaire consisted of three main categories: sociodemographic characteristics of the participants, practice of AI programs, and perceptions of AI programs, including ChatGPT. Results A total of 321 students responded to the distributed questionnaire, and 261 participants (81.3%) stated that they had heard about AI programs. In addition, 135 participants had used AI programs before (42.1%), while less than half the participants used them in their university studies (44.2%): for drug information (44.5%), homework (38.9%), and writing research articles (39.3%). There was significantly (48.3%, P<0.005) more conviction in the use of AI programs for writing research articles among pharmacy students from Palestine compared to Jordan. Lastly, there was significantly more (53.8%, P<0.05) AI program use among medicine students than pharmacy students. Conclusion While most medicine and pharmacy students had heard about AI programs, only a small proportion of the participants had used them in their medical study. In addition, attitudes and practice related to AI programs in their education differs between medicine and pharmacy students and between WBP and Jordan.
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Affiliation(s)
- Rami Mosleh
- Department of Pharmacy, Faculty of Medicine and Health Sciences, An-Najah National University, Nablus, Palestine
| | - Qais Jarrar
- Department of Pharmaceutical sciences, Faculty of Pharmacy, Isra University, Amman, Jordan
| | - Yazun Jarrar
- Department of Basic Medical Sciences, Faculty of Medicine, Al-Balqa Applied University, Al-Salt, Jordan
| | - Mariam Tazkarji
- Faculty of Science, McMaster University, Hamilton, Ontario, Canada
| | - Mohammed Hawash
- Department of Pharmacy, Faculty of Medicine and Health Sciences, An-Najah National University, Nablus, Palestine
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Sarfaraz S, Khurshid Z, Zafar MS. Use of artificial intelligence in medical education: A strength or an infirmity. J Taibah Univ Med Sci 2023; 18:1553-1554. [PMID: 37701848 PMCID: PMC10494170 DOI: 10.1016/j.jtumed.2023.06.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Indexed: 09/14/2023] Open
Affiliation(s)
- Shaur Sarfaraz
- Department of Medical Education, Altamash Institute of Dental Medicine, Karachi 74200, Pakistan
| | - Zohaib Khurshid
- Department of Prosthodontics and Implantology, College of Dentistry, King Faisal University, Al-Ahsa 31982, KSA
| | - Muhammad S. Zafar
- Department of Restorative, Dentistry, Taibah University, Almadinah Almunawwarah, KSA
- Department of Dental Materials, Islamic International Dental College, Riphah International University, Islamabad, Pakistan
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Mohanasundari SK, Kalpana M, Madhusudhan U, Vasanthkumar K, B R, Singh R, Vashishtha N, Bhatia V. Can Artificial Intelligence Replace the Unique Nursing Role? Cureus 2023; 15:e51150. [PMID: 38283483 PMCID: PMC10811613 DOI: 10.7759/cureus.51150] [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: 12/26/2023] [Indexed: 01/30/2024] Open
Abstract
Artificial intelligence (AI) is transforming healthcare, offering potential benefits and challenges. In healthcare, AI enhances efficiency, streamlines processes, and supports decision-making. However, challenges include potential errors and biases in algorithms, data privacy concerns, legal and ethical issues, and resistance to change. In nursing, a delicate balance emerges between AI and human touch. While AI aids in data-driven decision-making and administrative tasks, it lacks the emotional intelligence, empathy, and nuanced understanding crucial to nursing care. Nurses excel in critical thinking, adaptability to dynamic situations, patient advocacy, collaboration, and establishing human connections. AI supports these functions by automating routine tasks and offering decision support tools, yet its rigidity in dynamic situations and lack of human touch pose limitations. This review underscores the necessity of careful AI integration in healthcare, acknowledging its advantages while mitigating drawbacks. In nursing, the symbiosis between AI and human qualities is vital. The role of AI should be to complement, not replace, the unique skills and empathetic aspects of nursing care. Addressing concerns related to bias, transparency, data privacy, and professional training is essential for maximizing the benefits of AI in healthcare while preserving the human touch in patient care. This article explores whether AI can replace unique nursing roles.
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Affiliation(s)
- S K Mohanasundari
- Pediatric Nursing, All India Institute of Medical Sciences, Bibinagar, Bibinagar, IND
| | - M Kalpana
- Physiology, All India Institute of Medical Sciences, Bibinagar, Bibinagar, IND
| | - U Madhusudhan
- Physiology, All India Institute of Medical Sciences, Bibinagar, Bibinagar, IND
| | - Kasturi Vasanthkumar
- Psychiatric Nursing, All India Institute of Medical Sciences, Bibinagar, Bibinagar, IND
| | - Rani B
- Community Health Nursing, All India Institute of Medical Sciences, Bibinagar, Bibinagar, IND
| | - Rashmi Singh
- Obstetrics and Gynaecology Nursing, All India Institute of Medical Sciences, Bibinagar, Bibinagar, IND
| | - Neelam Vashishtha
- Medical Surgical Nursing, All India Institute of Medical Sciences, Bibinagar, Bibinagar, IND
| | - Vikas Bhatia
- Community Medicine and Family Medicine, All India Institute of Medical Sciences, Bibinagar, Bibinagar, IND
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Sallam M, Al-Salahat K, Al-Ajlouni E. ChatGPT Performance in Diagnostic Clinical Microbiology Laboratory-Oriented Case Scenarios. Cureus 2023; 15:e50629. [PMID: 38107211 PMCID: PMC10725273 DOI: 10.7759/cureus.50629] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/16/2023] [Indexed: 12/19/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI)-based tools can reshape healthcare practice. This includes ChatGPT which is considered among the most popular AI-based conversational models. Nevertheless, the performance of different versions of ChatGPT needs further evaluation in different settings to assess its reliability and credibility in various healthcare-related tasks. Therefore, the current study aimed to assess the performance of the freely available ChatGPT-3.5 and the paid version ChatGPT-4 in 10 different diagnostic clinical microbiology case scenarios. METHODS The current study followed the METRICS (Model, Evaluation, Timing/Transparency, Range/Randomization, Individual factors, Count, Specificity of the prompts/language) checklist for standardization of the design and reporting of AI-based studies in healthcare. The models tested on December 3, 2023 included ChatGPT-3.5 and ChatGPT-4 and the evaluation of the ChatGPT-generated content was based on the CLEAR tool (Completeness, Lack of false information, Evidence support, Appropriateness, and Relevance) assessed on a 5-point Likert scale with a range of the CLEAR scores of 1-5. ChatGPT output was evaluated by two raters independently and the inter-rater agreement was based on the Cohen's κ statistic. Ten diagnostic clinical microbiology laboratory case scenarios were created in the English language by three microbiologists at diverse levels of expertise following an internal discussion of common cases observed in Jordan. The range of topics included bacteriology, mycology, parasitology, and virology cases. Specific prompts were tailored based on the CLEAR tool and a new session was selected following prompting each case scenario. RESULTS The Cohen's κ values for the five CLEAR items were 0.351-0.737 for ChatGPT-3.5 and 0.294-0.701 for ChatGPT-4 indicating fair to good agreement and suitability for analysis. Based on the average CLEAR scores, ChatGPT-4 outperformed ChatGPT-3.5 (mean: 2.64±1.06 vs. 3.21±1.05, P=.012, t-test). The performance of each model varied based on the CLEAR items, with the lowest performance for the "Relevance" item (2.15±0.71 for ChatGPT-3.5 and 2.65±1.16 for ChatGPT-4). A statistically significant difference upon assessing the performance per each CLEAR item was only seen in ChatGPT-4 with the best performance in "Completeness", "Lack of false information", and "Evidence support" (P=0.043). The lowest level of performance for both models was observed with antimicrobial susceptibility testing (AST) queries while the highest level of performance was seen in bacterial and mycologic identification. CONCLUSIONS Assessment of ChatGPT performance across different diagnostic clinical microbiology case scenarios showed that ChatGPT-4 outperformed ChatGPT-3.5. The performance of ChatGPT demonstrated noticeable variability depending on the specific topic evaluated. A primary shortcoming of both ChatGPT models was the tendency to generate irrelevant content lacking the needed focus. Although the overall ChatGPT performance in these diagnostic microbiology case scenarios might be described as "above average" at best, there remains a significant potential for improvement, considering the identified limitations and unsatisfactory results in a few cases.
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Affiliation(s)
- Malik Sallam
- Department of Pathology, Microbiology and Forensic Medicine, The University of Jordan, School of Medicine, Amman, JOR
- Department of Clinical Laboratories and Forensic Medicine, Jordan University Hospital, Amman, JOR
| | - Khaled Al-Salahat
- Department of Pathology, Microbiology and Forensic Medicine, The University of Jordan, School of Medicine, Amman, JOR
| | - Eyad Al-Ajlouni
- Department of Pathology, Microbiology and Forensic Medicine, The University of Jordan, School of Medicine, Amman, JOR
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Diwakar M, Singh P, Ravi V. Medical Data Analysis Meets Artificial Intelligence (AI) and Internet of Medical Things (IoMT). Bioengineering (Basel) 2023; 10:1370. [PMID: 38135961 PMCID: PMC10740669 DOI: 10.3390/bioengineering10121370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 11/27/2023] [Indexed: 12/24/2023] Open
Abstract
AI is a contemporary methodology rooted in the field of computer science [...].
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Affiliation(s)
- Manoj Diwakar
- Department of Computer Science and Engineering, Graphic Era Deemed to Be University, Dehradun 248002, India
| | - Prabhishek Singh
- School of Computer Science Engineering and Technology, Bennett University, Greater Noida 201310, India;
| | - Vinayakumar Ravi
- Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar 34754, Saudi Arabia;
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Li J. Security Implications of AI Chatbots in Health Care. J Med Internet Res 2023; 25:e47551. [PMID: 38015597 PMCID: PMC10716748 DOI: 10.2196/47551] [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: 03/24/2023] [Revised: 08/30/2023] [Accepted: 11/20/2023] [Indexed: 11/29/2023] Open
Abstract
Artificial intelligence (AI) chatbots like ChatGPT and Google Bard are computer programs that use AI and natural language processing to understand customer questions and generate natural, fluid, dialogue-like responses to their inputs. ChatGPT, an AI chatbot created by OpenAI, has rapidly become a widely used tool on the internet. AI chatbots have the potential to improve patient care and public health. However, they are trained on massive amounts of people's data, which may include sensitive patient data and business information. The increased use of chatbots introduces data security issues, which should be handled yet remain understudied. This paper aims to identify the most important security problems of AI chatbots and propose guidelines for protecting sensitive health information. It explores the impact of using ChatGPT in health care. It also identifies the principal security risks of ChatGPT and suggests key considerations for security risk mitigation. It concludes by discussing the policy implications of using AI chatbots in health care.
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Affiliation(s)
- Jingquan Li
- Hofstra University, Hempstead, NY, United States
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Heboyan A, Yazdanie N, Ahmed N. Glimpse into the future of prosthodontics: The synergy of artificial intelligence. World J Clin Cases 2023; 11:7940-7942. [DOI: 10.12998/wjcc.v11.i33.7940] [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: 09/21/2023] [Revised: 10/26/2023] [Accepted: 11/17/2023] [Indexed: 11/24/2023] Open
Abstract
Prosthodontics, deals in the restoration and replacement of missing and structurally compromised teeth, this field has been remarkably transformed in the last two decades. Through the integration of digital imaging and three-dimensional printing, prosthodontics has evolved to provide more durable, precise, and patient-centric outcome. However, as we stand at the convergence of technology and healthcare, a new era is emerging, one that holds immense promise for the field and that is artificial intelligence (AI). In this paper, we explored the fascinating challenges and prospects associated with the future of prosthodontics in the era of AI.
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Affiliation(s)
- Artak Heboyan
- Department of Prosthodontics, Yerevan State Medical University after Mkhitar Heratsi, Yerevan 0025, Armenia
| | - Nazia Yazdanie
- Department of Prosthodontics, FMH College of Medicine and Dentistry, Lahore 54000, Pakistan
| | - Naseer Ahmed
- Department of Prosthodontics, Altammash Institute of Dental Medicine, Karachi 75500, Pakistan
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Sallam M, Barakat M, Sallam M. Pilot Testing of a Tool to Standardize the Assessment of the Quality of Health Information Generated by Artificial Intelligence-Based Models. Cureus 2023; 15:e49373. [PMID: 38024074 PMCID: PMC10674084 DOI: 10.7759/cureus.49373] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/24/2023] [Indexed: 12/01/2023] Open
Abstract
Background Artificial intelligence (AI)-based conversational models, such as Chat Generative Pre-trained Transformer (ChatGPT), Microsoft Bing, and Google Bard, have emerged as valuable sources of health information for lay individuals. However, the accuracy of the information provided by these AI models remains a significant concern. This pilot study aimed to test a new tool with key themes for inclusion as follows: Completeness of content, Lack of false information in the content, Evidence supporting the content, Appropriateness of the content, and Relevance, referred to as "CLEAR", designed to assess the quality of health information delivered by AI-based models. Methods Tool development involved a literature review on health information quality, followed by the initial establishment of the CLEAR tool, which comprised five items that aimed to assess the following: completeness, lack of false information, evidence support, appropriateness, and relevance. Each item was scored on a five-point Likert scale from excellent to poor. Content validity was checked by expert review. Pilot testing involved 32 healthcare professionals using the CLEAR tool to assess content on eight different health topics deliberately designed with varying qualities. The internal consistency was checked with Cronbach's alpha (α). Feedback from the pilot test resulted in language modifications to improve the clarity of the items. The final CLEAR tool was used to assess the quality of health information generated by four distinct AI models on five health topics. The AI models were ChatGPT 3.5, ChatGPT 4, Microsoft Bing, and Google Bard, and the content generated was scored by two independent raters with Cohen's kappa (κ) for inter-rater agreement. Results The final five CLEAR items were: (1) Is the content sufficient?; (2) Is the content accurate?; (3) Is the content evidence-based?; (4) Is the content clear, concise, and easy to understand?; and (5) Is the content free from irrelevant information? Pilot testing on the eight health topics revealed acceptable internal consistency with a Cronbach's α range of 0.669-0.981. The use of the final CLEAR tool yielded the following average scores: Microsoft Bing (mean=24.4±0.42), ChatGPT-4 (mean=23.6±0.96), Google Bard (mean=21.2±1.79), and ChatGPT-3.5 (mean=20.6±5.20). The inter-rater agreement revealed the following Cohen κ values: for ChatGPT-3.5 (κ=0.875, P<.001), ChatGPT-4 (κ=0.780, P<.001), Microsoft Bing (κ=0.348, P=.037), and Google Bard (κ=.749, P<.001). Conclusions The CLEAR tool is a brief yet helpful tool that can aid in standardizing testing of the quality of health information generated by AI-based models. Future studies are recommended to validate the utility of the CLEAR tool in the quality assessment of AI-generated health-related content using a larger sample across various complex health topics.
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Affiliation(s)
- Malik Sallam
- Department of Pathology, Microbiology, and Forensic Medicine, School of Medicine, University of Jordan, Amman, JOR
- Department of Clinical Laboratories and Forensic Medicine, Jordan University Hospital, Amman, JOR
| | - Muna Barakat
- Department of Clinical Pharmacy and Therapeutics, School of Pharmacy, Applied Science Private University, Amman, JOR
- Department of Research, Middle East University, Amman, JOR
| | - Mohammed Sallam
- Department of Pharmacy, Mediclinic Parkview Hospital, Mediclinic Middle East, Dubai, ARE
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Yelne S, Chaudhary M, Dod K, Sayyad A, Sharma R. Harnessing the Power of AI: A Comprehensive Review of Its Impact and Challenges in Nursing Science and Healthcare. Cureus 2023; 15:e49252. [PMID: 38143615 PMCID: PMC10744168 DOI: 10.7759/cureus.49252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 11/22/2023] [Indexed: 12/26/2023] Open
Abstract
This comprehensive review delves into the impact and challenges of Artificial Intelligence (AI) in nursing science and healthcare. AI has already demonstrated its transformative potential in these fields, with applications spanning from personalized care and diagnostic accuracy to predictive analytics and telemedicine. However, the integration of AI has its complexities, including concerns related to data privacy, ethical considerations, and biases in algorithms and datasets. The future of healthcare appears promising, with AI poised to advance diagnostics, treatment, and healthcare practices. Nevertheless, it is crucial to remember that AI should complement, not replace, healthcare professionals, preserving the essential human element of care. To maximize AI's potential in healthcare, interdisciplinary collaboration, ethical guidelines, and the protection of patient rights are essential. This review concludes with a call to action, emphasizing the need for ongoing research and collective efforts to ensure that AI contributes to improved healthcare outcomes while upholding the highest standards of ethics and patient-centered care.
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Affiliation(s)
- Seema Yelne
- Nursing, Shalinitai Meghe College of Nursing, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Minakshi Chaudhary
- Nursing, Shalinitai Meghe College of Nursing, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Karishma Dod
- Nursing, Shalinitai Meghe College of Nursing, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Akhtaribano Sayyad
- Nursing, Shalinitai Meghe College of Nursing, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Ranjana Sharma
- Nursing, Shalinitai Meghe College of Nursing, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Winkelman J, Nguyen D, vanSonnenberg E, Kirk A, Lieberman S. Artificial Intelligence (AI) in pediatric endocrinology. J Pediatr Endocrinol Metab 2023; 36:903-908. [PMID: 37589444 DOI: 10.1515/jpem-2023-0287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 08/03/2023] [Indexed: 08/18/2023]
Abstract
Artificial Intelligence (AI) is integrating itself throughout the medical community. AI's ability to analyze complex patterns and interpret large amounts of data will have considerable impact on all areas of medicine, including pediatric endocrinology. In this paper, we review and update the current studies of AI in pediatric endocrinology. Specific topics that are addressed include: diabetes management, bone growth, metabolism, obesity, and puberty. Becoming knowledgeable and comfortable with AI will assist pediatric endocrinologists, the goal of the paper.
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Affiliation(s)
| | - Diep Nguyen
- University of Arizona College of Medicine Phoenix, Phoenix, USA
| | - Eric vanSonnenberg
- University of Arizona College of Medicine Phoenix, Phoenix, USA
- From the Departments of Radiology, University of Arizona College of Medicine Phoenix, Phoenix, USA
- Student Affairs, University of Arizona College of Medicine Phoenix, Phoenix, USA
| | - Alison Kirk
- University of Arizona College of Medicine Phoenix, Phoenix, USA
- Student Affairs, University of Arizona College of Medicine Phoenix, Phoenix, USA
- Pediatrics, University of Arizona College of Medicine Phoenix, Phoenix, USA
| | - Steven Lieberman
- University of Arizona College of Medicine Phoenix, Phoenix, USA
- Internal Medicine (Division of Endocrinology), University of Arizona College of Medicine Phoenix, Phoenix, USA
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47
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Vadhwana B, Tarazi M, Patel V. The Role of Artificial Intelligence in Prospective Real-Time Histological Prediction of Colorectal Lesions during Colonoscopy: A Systematic Review and Meta-Analysis. Diagnostics (Basel) 2023; 13:3267. [PMID: 37892088 PMCID: PMC10606449 DOI: 10.3390/diagnostics13203267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 10/16/2023] [Accepted: 10/19/2023] [Indexed: 10/29/2023] Open
Abstract
Artificial intelligence (AI) presents a novel platform for improving disease diagnosis. However, the clinical utility of AI remains limited to discovery studies, with poor translation to clinical practice. Current data suggests that 26% of diminutive pre-malignant lesions and 3.5% of colorectal cancers are missed during colonoscopies. The primary aim of this study was to explore the role of artificial intelligence in real-time histological prediction of colorectal lesions during colonoscopy. A systematic search using MeSH headings relating to "AI", "machine learning", "computer-aided", "colonoscopy", and "colon/rectum/colorectal" identified 2290 studies. Thirteen studies reporting real-time analysis were included. A total of 2958 patients with 5908 colorectal lesions were included. A meta-analysis of six studies reporting sensitivities (95% CI) demonstrated that endoscopist diagnosis was superior to a computer-assisted detection platform, although no statistical significance was reached (p = 0.43). AI applications have shown encouraging results in differentiating neoplastic and non-neoplastic lesions using narrow-band imaging, white light imaging, and blue light imaging. Other modalities include autofluorescence imaging and elastic scattering microscopy. The current literature demonstrates that despite the promise of new endoscopic AI models, they remain inferior to expert endoscopist diagnosis. There is a need to focus developments on real-time histological predictions prior to clinical translation to demonstrate improved diagnostic capabilities and time efficiency.
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Affiliation(s)
- Bhamini Vadhwana
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0HS, UK
| | - Munir Tarazi
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0HS, UK
| | - Vanash Patel
- Department of Surgery and Cancer, Imperial College London, Du Cane Road, London W12 0HS, UK
- West Hertfordshire Hospital NHS Trust, Vicarage Road, Watford WD18 0HB, UK
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48
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Chang MC, Kim JK, Park D, Kim JH, Kim CR, Choo YJ. The Use of Artificial Intelligence to Predict the Prognosis of Patients Undergoing Central Nervous System Rehabilitation: A Narrative Review. Healthcare (Basel) 2023; 11:2687. [PMID: 37830724 PMCID: PMC10572243 DOI: 10.3390/healthcare11192687] [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: 09/01/2023] [Revised: 09/27/2023] [Accepted: 09/30/2023] [Indexed: 10/14/2023] Open
Abstract
Applications of machine learning in the healthcare field have become increasingly diverse. In this review, we investigated the integration of artificial intelligence (AI) in predicting the prognosis of patients with central nervous system disorders such as stroke, traumatic brain injury, and spinal cord injury. AI algorithms have shown promise in prognostic assessment, but challenges remain in achieving a higher prediction accuracy for practical clinical use. We suggest that accumulating more diverse data, including medical imaging and collaborative efforts among hospitals, can enhance the predictive capabilities of AI. As healthcare professionals become more familiar with AI, its role in central nervous system rehabilitation is expected to advance significantly, revolutionizing patient care.
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Affiliation(s)
- Min Cheol Chang
- Department of Rehabilitation Medicine, College of Medicine, Yeungnam University, Daegu 42415, Republic of Korea;
| | - Jeoung Kun Kim
- Department of Business Administration, School of Business, Yeungnam University, Gyeongsan-si 38541, Republic of Korea;
| | - Donghwi Park
- Department of Rehabilitation Medicine, Daegu Fatima Hospital, Daegu 41199, Republic of Korea;
| | - Jang Hwan Kim
- Department of Rehabilitation Technology, Graduate School of Hanseo University, Seosan, Chungcheongnam-do 31962, Republic of Korea;
| | - Chung Reen Kim
- Department of Physical Medicine and Rehabilitation, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan 44033, Republic of Korea;
| | - Yoo Jin Choo
- Department of Rehabilitation Medicine, College of Medicine, Yeungnam University, Daegu 42415, Republic of Korea;
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Mithany RH, Aslam S, Abdallah S, Abdelmaseeh M, Gerges F, Mohamed MS, Manasseh M, Wanees A, Shahid MH, Khalil MS, Daniel N. Advancements and Challenges in the Application of Artificial Intelligence in Surgical Arena: A Literature Review. Cureus 2023; 15:e47924. [PMID: 37908699 PMCID: PMC10613559 DOI: 10.7759/cureus.47924] [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: 10/29/2023] [Indexed: 11/02/2023] Open
Abstract
This literature review delves into the transformative potential of artificial intelligence (AI) in the field of surgery, exploring its evolution, applications, and technological advancements. AI, with its ability to mimic human intelligence, presents a paradigm shift in surgical practices. The review critically analyzes a broad range of research, encompassing machine learning, deep learning, natural language processing, and their diverse applications in preoperative planning, surgical simulation, intraoperative guidance, and postoperative analysis. Ethical, legal, and regulatory considerations, as well as challenges and future directions, are also explored. The study underscores AI's ability to revolutionize surgical visualization and its role in shaping the future of healthcare.
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Affiliation(s)
- Reda H Mithany
- Laparoscopic Colorectal Surgery, Kingston Hospital National Health Service (NHS) Foundation Trust, Kingston Upon Thames, GBR
| | - Samana Aslam
- General Surgery, Lahore General Hospital, Lahore, PAK
| | | | - Mark Abdelmaseeh
- General Surgery, Faculty of Medicine, Assuit University, Assuit, EGY
| | - Farid Gerges
- General and Emergency Surgery, Kingston Hospital National Health Service (NHS) Foundation Trust, Kingston Upon Thames, GBR
| | | | - Mina Manasseh
- General Surgery, Torbay and South Devon National Health Service (NHS) Foundation Trust, Torquay, GBR
| | | | | | | | - Nesma Daniel
- Medical Laboratory Science, Ain Shams Hospital, Cairo, EGY
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50
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Park S, Kim JH, Cha YK, Chung MJ, Woo JH, Park S. Application of Machine Learning Algorithm in Predicting Axillary Lymph Node Metastasis from Breast Cancer on Preoperative Chest CT. Diagnostics (Basel) 2023; 13:2953. [PMID: 37761320 PMCID: PMC10528867 DOI: 10.3390/diagnostics13182953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 09/05/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
Axillary lymph node (ALN) status is one of the most critical prognostic factors in patients with breast cancer. However, ALN evaluation with contrast-enhanced CT (CECT) has been challenging. Machine learning (ML) is known to show excellent performance in image recognition tasks. The purpose of our study was to evaluate the performance of the ML algorithm for predicting ALN metastasis by combining preoperative CECT features of both ALN and primary tumor. This was a retrospective single-institutional study of a total of 266 patients with breast cancer who underwent preoperative chest CECT. Random forest (RF), extreme gradient boosting (XGBoost), and neural network (NN) algorithms were used. Statistical analysis and recursive feature elimination (RFE) were adopted as feature selection for ML. The best ML-based ALN prediction model for breast cancer was NN with RFE, which achieved an AUROC of 0.76 ± 0.11 and an accuracy of 0.74 ± 0.12. By comparing NN with RFE model performance with and without ALN features from CECT, NN with RFE model with ALN features showed better performance at all performance evaluations, which indicated the effect of ALN features. Through our study, we were able to demonstrate that the ML algorithm could effectively predict the final diagnosis of ALN metastases from CECT images of the primary tumor and ALN. This suggests that ML has the potential to differentiate between benign and malignant ALNs.
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Affiliation(s)
- Soyoung Park
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul 06351, Republic of Korea; (S.P.); (S.P.)
| | - Jong Hee Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea; (J.H.K.); (J.H.W.)
| | - Yoon Ki Cha
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea; (J.H.K.); (J.H.W.)
| | - Myung Jin Chung
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea; (J.H.K.); (J.H.W.)
| | - Jung Han Woo
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea; (J.H.K.); (J.H.W.)
| | - Subin Park
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul 06351, Republic of Korea; (S.P.); (S.P.)
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