Med-HEAL: Analyzing and Mitigating Hallucinations in Medical LLMs with Hallucination-Aware In-Context Learning
arXiv:2606.01301v1 Announce Type: new Abstract: Hallucinations in medical large language models (LLMs) pose serious risks for clinical decision support, particularly when models must reason over complex electronic health records (EHRs). However, existing benchmarks often lack a realistic clinical context and provide limited insight into how hallucinations can be mitigated in practice. We introduce Med-HEAL, a framework for systematically identifying, analyzing, and mitigating hallucinations in medical LLMs using clinically grounded data. Building on the EHRNoteQA benchmark derived from MIMIC-I
The proliferation of LLMs in sensitive domains like healthcare necessitates robust methods for identifying and mitigating their inherent biases and inaccuracies, especially as regulatory pushes for AI safety intensify.
Reliable medical LLMs are crucial for clinical decision support, and addressing hallucinations directly impacts patient safety, diagnostic accuracy, and user trust in AI-powered healthcare solutions.
This research introduces concrete methods and benchmarks (Med-HEAL, EHRNoteQA) to systematically analyze and reduce harmful LLM hallucinations, providing a pathway for more trustworthy and deployable medical AI.
- · Healthcare AI developers
- · Medical professionals leveraging AI
- · Patients
- · AI safety researchers
- · LLMs without robust hallucination mitigation
- · Healthcare providers relying on unaudited AI
- · Companies offering unsafe AI products
Improved reliability and wider adoption of AI in medical diagnostics and clinical support.
Increased regulatory scrutiny and standardization efforts for AI safety in highly sensitive sectors like healthcare.
Enhanced trust in AI systems leading to a redefinition of human-AI collaboration in complex professional fields.
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Read at arXiv cs.CL