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arXiv · 2506.11129

Trustworthy AI for Medicine: Continuous Hallucination Detection and Elimination with CHECK

Carlos Garcia-Fernandez·Luis Felipe·Monique Shotande·Muntasir Zitu·Aakash Tripathi·Ghulam Rasool·Issam El Naqa·Vivek Rudrapatna·Gilmer Valdes
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Abstract

Large language models (LLMs) show promise in healthcare, but hallucinations remain a major barrier to clinical use. We present CHECK, a continuous-learning framework that integrates structured clinical databases with a classifier grounded in information theory to detect both factual and reasoning-based hallucinations. Evaluated on 1500 questions from 100 pivotal clinical trials, CHECK reduced LLama3.3-70B-Instruct hallucination rates from 31% to 0.3% - making an open source model state of the art. Its classifier generalized across medical benchmarks, achieving AUCs of 0.95-0.96, including on the MedQA (USMLE) benchmark and HealthBench realistic multi-turn medical questioning. By leveraging hallucination probabilities to guide GPT-4o's refinement and judiciously escalate compute, CHECK boosted its USMLE passing rate by 5 percentage points, achieving a state-of-the-art 92.1%. By suppressing hallucinations below accepted clinical error thresholds, CHECK offers a scalable foundation for safe LLM deployment in medicine and other high-stakes domains.

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Carlos Garcia-Fernandez, Luis Felipe, Monique Shotande, Muntasir Zitu, Aakash Tripathi, Ghulam Rasool, Issam El Naqa, Vivek Rudrapatna, Gilmer Valdes. 2025-06-10. Trustworthy AI for Medicine: Continuous Hallucination Detection and Elimination with CHECK. https://arxiv.org/abs/2506.11129

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