ArticleJournal of general internal medicine2025
Bias Sensitivity in Diagnostic Decision-Making: Comparing ChatGPT with Residents.
Article in Journal of general internal medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Real-Time Artificial Intelligence Diagnostic Copilot in Simulated Primary Care Consultations: Randomized Simulation Study.JMIR formative research · 2026Trial
- Diagnostic performance of large language models for discrepancy detection in non-English resident-authored abdominal CT and MRI reports: a Turkish-language evaluation.Abdominal radiology (New York) · 2026Article
- Biases of large language models in diagnosing Cushing's syndrome.World journal of methodology · 2026Review
- ChatGPT Health performance in a structured test of triage recommendations.Nature medicine · 2026Article
- Human-large language model collaboration in clinical medicine: a systematic review and meta-analysis.NPJ digital medicine · 2026Article
- ChatGPT, Gemini, and Grok on familial mediterranean fever: are they trustworthy?Clinical rheumatology · 2026Article
- Implicit bias in safety-aligned large language models: A multi-faceted evaluation of clinical decision-making and health equity.PloS one · 2026Article
- Cognitive bias in clinical large language models.NPJ digital medicine · 2025Article
- ChatGPT in Academic Writing: A Scientometric Analysis of Literature Published Between 2022 and 2023.Journal of empirical research on human research ethics : JERHRE · 2025Article
- Making large language models into reliable physician assistants.Nature medicine · 2025Article
- A systematic review and meta-analysis of GPT-based differential diagnostic accuracy in radiological cases: 2023-2025.Frontiers in radiology · 2025Article
- Comparing Artificial intelligence to physicians' competences in the domain of clinical reasoning: A systematic review and meta-analysis.Journal of medical education and curricular developmentReview
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3 authors.
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Abstract
backgroundDiagnostic errors, often due to biases in clinical reasoning, significantly affect patient care. While artificial intelligence chatbots like ChatGPT could help mitigate such biases, their potential susceptibility to biases is unknown.
methodsThis study evaluated diagnostic accuracy of ChatGPT against the performance of 265 medical residents in five previously published experiments aimed at inducing bias. The residents worked in several major teaching hospitals in the Netherlands. The biases studied were case-intrinsic (presence of salient distracting findings in the patient history, effects of disruptive patient behaviors) and situational (prior availability of a look-alike patient). ChatGPT's accuracy in identifying the most-likely diagnosis was measured.
resultsDiagnostic accuracy of residents and ChatGPT was equivalent. For clinical cases involving case-intrinsic bias, both ChatGPT and the residents exhibited a decline in diagnostic accuracy. Residents' accuracy decreased on average 12%, while the accuracy of ChatGPT 4.0 decreased 21%. Accuracy of ChatGPT 3.5 decreased 9%. These findings suggest that, like human diagnosticians, ChatGPT is sensitive to bias when the biasing information is part of the patient history. When the biasing information was extrinsic to the case in the form of the prior availability of a look-alike case, residents' accuracy decreased by 15%. By contrast, ChatGPT's performance was not affected by the biasing information. Chi-square goodness-of-fit tests corroborated these outcomes.
conclusionsIt seems that, while ChatGPT is not sensitive to bias when biasing information is situational, it is sensitive to bias when the biasing information is part of the patient's disease history. Its utility in diagnostic support has potential, but caution is advised. Future research should enhance AI's bias detection and mitigation to make it truly useful for diagnostic support.
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