Benefits, limits, and risks of ChatGPT in medicine

被引:1
|
作者
Tangsrivimol, Jonathan A. [1 ,2 ]
Darzidehkalani, Erfan [3 ]
Virk, Hafeez Ul Hassan [4 ]
Wang, Zhen [5 ,6 ]
Egger, Jan [7 ]
Wang, Michelle [8 ]
Hacking, Sean [9 ]
Glicksberg, Benjamin S. [10 ]
Strauss, Markus [11 ,12 ]
Krittanawong, Chayakrit [13 ,14 ]
机构
[1] Weill Cornell Med, NewYork Presbyterian Hosp, Dept Neurosurg & Neurosci, New York, NY USA
[2] Chulabhorn Royal Acad, Chulabhorn Hosp, Dept Neurosurg, Bangkok, Thailand
[3] MIT, MIT Comp Sci & Artificial Intelligence Lab, Cambridge, MA USA
[4] Case Western Reserve Univ, Univ Hosp Cleveland Med Ctr, Harrington Heart & Vasc Inst, Cleveland Hts, OH USA
[5] Mayo Clin, Robert D & Patricia E Kern Ctr Sci Hlth Care Deli, Rochester, MN USA
[6] Mayo Clin, Dept Hlth Sci Res, Div Hlth Care Policy & Res, Rochester, MN USA
[7] Univ Hosp Essen AoR, Inst Artificial Intelligence Med, Essen, Germany
[8] Univ Calif San Francisco, Bakar Computat Hlth Sci Inst, San Francisco, CA USA
[9] NYU, Dept Pathol, Grossman Sch Med, New York, NY USA
[10] Icahn Sch Med Mt Sinai, Hasso Plattner Inst Digital Hlth, New York, NY USA
[11] Univ Hosp Muenster, Dept Cardiol Coronary & Peripheral Vasc Dis 1, Heart Failure Med, Munster, Germany
[12] Univ Witten Herdecke, Fac Hlth, Sch Med, Dept Cardiol,Sect Prevent Med Hlth Promot, Hagen, Germany
[13] NYU, New York Univ Langone Hlth, Cardiol Div, Sch Med, New York, NY 10016 USA
[14] HumanX, Newark, DE 19716 USA
来源
关键词
large language models; deep learning; artificial intelligence; ChatGPT; healthcare questions; healthcare; medicine; ARTIFICIAL-INTELLIGENCE; EDUCATION;
D O I
10.3389/frai.2025.1518049
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
ChatGPT represents a transformative technology in healthcare, with demonstrated impacts across clinical practice, medical education, and research. Studies show significant efficiency gains, including 70% reduction in administrative time for discharge summaries and achievement of medical professional-level performance on standardized tests (60% accuracy on USMLE, 78.2% on PubMedQA). ChatGPT offers personalized learning platforms, automated scoring, and instant access to vast medical knowledge in medical education, addressing resource limitations and enhancing training efficiency. It streamlines clinical workflows by supporting triage processes, generating discharge summaries, and alleviating administrative burdens, allowing healthcare professionals to focus more on patient care. Additionally, ChatGPT facilitates remote monitoring and chronic disease management, providing personalized advice, medication reminders, and emotional support, thus bridging gaps between clinical visits. Its ability to process and synthesize vast amounts of data accelerates research workflows, aiding in literature reviews, hypothesis generation, and clinical trial designs. This paper aims to gather and analyze published studies involving ChatGPT, focusing on exploring its advantages and disadvantages within the healthcare context. To aid in understanding and progress, our analysis is organized into six key areas: (1) Information and Education, (2) Triage and Symptom Assessment, (3) Remote Monitoring and Support, (4) Mental Healthcare Assistance, (5) Research and Decision Support, and (6) Language Translation. Realizing ChatGPT's full potential in healthcare requires addressing key limitations, such as its lack of clinical experience, inability to process visual data, and absence of emotional intelligence. Ethical, privacy, and regulatory challenges further complicate its integration. Future improvements should focus on enhancing accuracy, developing multimodal AI models, improving empathy through sentiment analysis, and safeguarding against artificial hallucination. While not a replacement for healthcare professionals, ChatGPT can serve as a powerful assistant, augmenting their expertise to improve efficiency, accessibility, and quality of care. This collaboration ensures responsible adoption of AI in transforming healthcare delivery. While ChatGPT demonstrates significant potential in healthcare transformation, systematic evaluation of its implementation across different healthcare settings reveals varying levels of evidence quality-from robust randomized trials in medical education to preliminary observational studies in clinical practice. This heterogeneity in evidence quality necessitates a structured approach to future research and implementation.
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页数:11
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