TY  - JOUR
AU  - Lahat, Adi
AU  - Sharif, Kassem
AU  - Zoabi, Narmin
AU  - Shneor Patt, Yonatan
AU  - Sharif, Yousra
AU  - Fisher, Lior
AU  - Shani, Uria
AU  - Arow, Mohamad
AU  - Levin, Roni
AU  - Klang, Eyal
PY  - 2024
DA  - 2024/6/27
TI  - Assessing Generative Pretrained Transformers (GPT) in Clinical Decision-Making: Comparative Analysis of GPT-3.5 and GPT-4
JO  - J Med Internet Res
SP  - e54571
VL  - 26
KW  - ChatGPT
KW  - chat-GPT
KW  - chatbot
KW  - chatbots
KW  - chat-bot
KW  - chat-bots
KW  - natural language processing
KW  - NLP
KW  - artificial intelligence
KW  - AI
KW  - machine learning
KW  - ML
KW  - algorithm
KW  - algorithms
KW  - predictive model
KW  - predictive models
KW  - predictive analytics
KW  - predictive system
KW  - practical model
KW  - practical models
KW  - internal medicine
KW  - ethics
KW  - ethical
KW  - ethical dilemma
KW  - ethical dilemmas
KW  - bioethics
KW  - emergency medicine
KW  - EM medicine
KW  - ED physician
KW  - emergency physician
KW  - emergency doctor
AB  - Background: Artificial intelligence, particularly chatbot systems, is becoming an instrumental tool in health care, aiding clinical decision-making and patient engagement. Objective: This study aims to analyze the performance of ChatGPT-3.5 and ChatGPT-4 in addressing complex clinical and ethical dilemmas, and to illustrate their potential role in health care decision-making while comparing seniors’ and residents’ ratings, and specific question types. Methods: A total of 4 specialized physicians formulated 176 real-world clinical questions. A total of 8 senior physicians and residents assessed responses from GPT-3.5 and GPT-4 on a 1-5 scale across 5 categories: accuracy, relevance, clarity, utility, and comprehensiveness. Evaluations were conducted within internal medicine, emergency medicine, and ethics. Comparisons were made globally, between seniors and residents, and across classifications. Results: Both GPT models received high mean scores (4.4, SD 0.8 for GPT-4 and 4.1, SD 1.0 for GPT-3.5). GPT-4 outperformed GPT-3.5 across all rating dimensions, with seniors consistently rating responses higher than residents for both models. Specifically, seniors rated GPT-4 as more beneficial and complete (mean 4.6 vs 4.0 and 4.6 vs 4.1, respectively; P<.001), and GPT-3.5 similarly (mean 4.1 vs 3.7 and 3.9 vs 3.5, respectively; P<.001). Ethical queries received the highest ratings for both models, with mean scores reflecting consistency across accuracy and completeness criteria. Distinctions among question types were significant, particularly for the GPT-4 mean scores in completeness across emergency, internal, and ethical questions (4.2, SD 1.0; 4.3, SD 0.8; and 4.5, SD 0.7, respectively; P<.001), and for GPT-3.5’s accuracy, beneficial, and completeness dimensions. Conclusions: ChatGPT’s potential to assist physicians with medical issues is promising, with prospects to enhance diagnostics, treatments, and ethics. While integration into clinical workflows may be valuable, it must complement, not replace, human expertise. Continued research is essential to ensure safe and effective implementation in clinical environments. 
SN  - 1438-8871
UR  - https://www.jmir.org/2024/1/e54571
UR  - https://doi.org/10.2196/54571
DO  - 10.2196/54571
ID  - info:doi/10.2196/54571
ER  -