Does ChatGPT Provide Appropriate and Equitable Medical Advice?: A Vignette-Based, Clinical Evaluation Across Care Contexts
Nastasi, A. J.; Courtright, K. R.; Halpern, S. D.; Weissman, G. E.
Show abstract
ChatGPT is a large language model trained on text corpora and reinforced with human supervision. Because ChatGPT can provide human-like responses to complex questions, it could become an easily accessible source of medical advice for patients. However, its ability to answer medical questions appropriately and equitably remains unknown. We presented ChatGPT with 96 advice-seeking vignettes that varied across clinical contexts, medical histories, and social characteristics. We analyzed responses for clinical appropriateness by concordance with guidelines, recommendation type, and consideration of social factors. Ninety-three (97%) responses were appropriate and did not explicitly violate clinical guidelines. Recommendations in response to advice-seeking questions were completely absent (N=34, 35%), general (N=18, 18%), or specific (N=44, 46%). Fifty-three (55%) explicitly considered social factors like race or insurance status, which in some cases changed clinical recommendations. ChatGPT consistently provided background information in response to medical questions but did not reliably offer appropriate and personalized medical advice.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- Cohort Design and Natural Language Processing to Reduce Bias in Electronic Health Records Research: The Community Care Cohort Project 93%
- Finding Long-COVID: Temporal Topic Modeling of Electronic Health Records from the N3C and RECOVER Programs 93%
- Evaluating large language model workflows in clinical decision support: referral, triage, and diagnosis 92%
Similar papers in this journal
- Identification of physiological adverse events using continuous vital signs monitoring during paediatric critical care transport: a novel data-driven approach 90%
- Modular Clinical Decision Support Networks (MoDN)—Updatable, Interpretable, and Portable Predictions for Evolving Clinical Environments 90%
- Community-acquired pneumonia identification from electronic health records in the absence of a gold standard: a Bayesian latent class analysis 90%
Similar papers in this journal
Similar papers in this journal
- Outcomes of a Smartphone-based Application with Live Health-Coaching Post-Percutaneous Coronary Intervention 93%
- Transformer-based deep learning model for the diagnosis of suspected lung cancer in primary care based on electronic health record data 92%
- Characterizing Long COVID: Deep Phenotype of a Complex Condition 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.