Medical Hallucinations in Foundation Models and Their Impact on Healthcare

Yubin Kim; Hyewon Jeong; Shan Chen; Shuyue Stella Li; Chanwoo Park; Mingyu Lu; Kumail Alhamoud; Jimin Mun; Cristina Grau; Minseok Jung; Rodrigo Gameiro; Lizhou Fan; Eugene Park; Tristan Lin; Joonsik Yoon; Wonjin Yoon; Maarten Sap; Yulia Tsvetkov; Paul Liang; Xuhai Xu; Xin Liu; Chunjong Park; Hyeonhoon Lee; Hae Won Park; Daniel McDuff; Samir Tulebaev; Cynthia Breazeal

arXiv · 2503.05777

Medical Hallucinations in Foundation Models and Their Impact on Healthcare

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Abstract

Hallucinations in foundation models arise from autoregressive training objectives that prioritize token-likelihood optimization over epistemic accuracy, fostering overconfidence and poorly calibrated uncertainty. We define medical hallucination as any model-generated output that is factually incorrect, logically inconsistent, or unsupported by authoritative clinical evidence in ways that could alter clinical decisions. We evaluated 11 foundation models (7 general-purpose, 4 medical-specialized) across seven medical hallucination tasks spanning medical reasoning and biomedical information retrieval. General-purpose models achieved significantly higher proportions of hallucination-free responses than medical-specialized models (median: 76.6% vs 51.3%, difference = 25.2%, 95% CI: 18.7-31.3%, Mann-Whitney U = 27.0, p = 0.012, rank-biserial r = -0.64). Top-performing models such as Gemini-2.5 Pro exceeded 97% accuracy when augmented with chain-of-thought prompting (base: 87.6%), while medical-specialized models like MedGemma ranged from 28.6-61.9% despite explicit training on medical corpora. Chain-of-thought reasoning significantly reduced hallucinations in 86.4% of tested comparisons after FDR correction (q < 0.05), demonstrating that explicit reasoning traces enable self-verification and error detection. Physician audits confirmed that 64-72% of residual hallucinations stemmed from causal or temporal reasoning failures rather than knowledge gaps. A global survey of clinicians (n = 70) validated real-world impact: 91.8% had encountered medical hallucinations, and 84.7% considered them capable of causing patient harm. The underperformance of medical-specialized models despite domain training indicates that safety emerges from sophisticated reasoning capabilities and broad knowledge integration developed during large-scale pre-training, not from narrow optimization.

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Yubin Kim, Hyewon Jeong, Shan Chen, Shuyue Stella Li, Chanwoo Park, Mingyu Lu, Kumail Alhamoud, Jimin Mun, Cristina Grau, Minseok Jung, Rodrigo Gameiro, Lizhou Fan, Eugene Park, Tristan Lin, Joonsik Yoon, Wonjin Yoon, Maarten Sap, Yulia Tsvetkov, Paul Liang, Xuhai Xu, Xin Liu, Chunjong Park, Hyeonhoon Lee, Hae Won Park, Daniel McDuff, Samir Tulebaev, Cynthia Breazeal. 2025-11-02. Medical Hallucinations in Foundation Models and Their Impact on Healthcare. https://arxiv.org/abs/2503.05777

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