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Assessing ChatGPT’s Responses to Otolaryngology Patient Questions

移情 耳鼻咽喉科 患者安全 医疗保健 医学 认证 心理学 医学教育 社会心理学 精神科 政治学 经济增长 经济 法学
作者
Jonathan M. Carnino,William R. Pellegrini,Megan L. Willis,Michael B. Cohen,Marianella Paz‐Lansberg,Elizabeth M. Davis,Gregory A. Grillone,Jessica R. Levi
出处
期刊:Annals of Otology, Rhinology, and Laryngology [SAGE Publishing]
标识
DOI:10.1177/00034894241249621
摘要

This study aims to evaluate ChatGPT's performance in addressing real-world otolaryngology patient questions, focusing on accuracy, comprehensiveness, and patient safety, to assess its suitability for integration into healthcare.A cross-sectional study was conducted using patient questions from the public online forum Reddit's r/AskDocs, where medical advice is sought from healthcare professionals. Patient questions were input into ChatGPT (GPT-3.5), and responses were reviewed by 5 board-certified otolaryngologists. The evaluation criteria included difficulty, accuracy, comprehensiveness, and bedside manner/empathy. Statistical analysis explored the relationship between patient question characteristics and ChatGPT response scores. Potentially dangerous responses were also identified.Patient questions averaged 224.93 words, while ChatGPT responses were longer at 414.93 words. The accuracy scores for ChatGPT responses were 3.76/5, comprehensiveness scores were 3.59/5, and bedside manner/empathy scores were 4.28/5. Longer patient questions did not correlate with higher response ratings. However, longer ChatGPT responses scored higher in bedside manner/empathy. Higher question difficulty correlated with lower comprehensiveness. Five responses were flagged as potentially dangerous.While ChatGPT exhibits promise in addressing otolaryngology patient questions, this study demonstrates its limitations, particularly in accuracy and comprehensiveness. The identification of potentially dangerous responses underscores the need for a cautious approach to AI in medical advice. Responsible integration of AI into healthcare necessitates thorough assessments of model performance and ethical considerations for patient safety.

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