arXiv:2606.12291v1 Announce Type: new Abstract: Large language models (LLMs) now reach expert-level scores on medical licensing exams, encouraging the assumption that high scores imply safe medical judgment while patients increasingly use them for health advice. We show this assumption is fragile: when misleading context is injected into questions that LLMs originally answer correctly, they abandon the correct answer. We call the ability to maintain correct judgment under adversarial context epistemic resilience, and introduce MedMisBench to measure it. MedMisBench contains 10,932 medical ques
The proliferation of LLMs in healthcare consultations and the increasing public reliance on them for medical advice necessitates a rigorous evaluation of their safety and reliability under adverse conditions.
This research reveals a critical vulnerability in LLM medical judgment, highlighting that high test scores do not equate to robust real-world performance, especially when confronted with misleading information.
The perceived infallibility of LLM 'expert' medical knowledge is undermined, forcing a re-evaluation of deployment strategies and the need for new benchmarks focusing on epistemic resilience rather than just factual accuracy.
- · AI safety researchers
- · Developers of robust AI architectures
- · Medical professionals emphasizing human oversight
- · LLM providers claiming unmitigated medical expertise
- · Patients relying solely on LLM medical advice
- · Healthcare systems integrating unvalidated LLMs heavily
This study will likely spur the development of new testing methodologies and regulatory frameworks for LLMs in sensitive domains like medicine.
It could lead to a 'trust crisis' in early LLM medical applications, increasing scrutiny and slowing broader adoption until resilience is proven.
The concept of 'epistemic resilience' may become a new, critical metric for AI evaluation across various high-stakes domains, driving fundamental shifts in AI development and benchmarking.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL