Stress-testing medical large language models reveals latent safety pathology beyond benchmark accuracy
arXiv:2606.07929v1 Announce Type: new Abstract: Large language models (LLMs) are entering clinical practice based on benchmark accuracy that may fail to detect safety-relevant failure modes. Here we present AI-MASLD, a stress-audit framework that adapts the logic of metabolic stress testing from hepatology to the evaluation of clinical LLMs. Using 240 clinical cases across six narrative perturbation probes, we subjected seven models to double-stress testing and quantified performance through three indices: metabolic index (MI), perturbation flip rate (PFR), and counterfactual fairness index (C
The rapid deployment of Large Language Models (LLMs) into sensitive applications like healthcare necessitates robust stress-testing methodologies to proactively identify safety risks beyond standard benchmarks.
This research provides a critical framework for evaluating the safety and reliability of clinical LLMs, highlighting potential 'latent safety pathology' which could have significant implications for patient care and regulatory approval.
The introduction of AI-MASLD shifts the focus from superficial accuracy metrics to deep stress-testing, pushing for more rigorous evaluation standards for AI in healthcare and potentially delaying or re-shaping future clinical deployments.
- · AI safety researchers
- · Medical regulatory bodies
- · Patients
- · AI audit and validation services
- · Developers rushing LLMs to market without extensive safety testing
- · Hospitals adopting unvalidated AI systems
- · Public trust in AI if failures occur due to lack of stress testing
Clinical LLM developers will need to integrate more comprehensive stress-testing frameworks like AI-MASLD into their development lifecycles.
This could lead to slower adoption of AI in certain high-stakes medical fields as regulatory bodies demand more stringent validation, potentially fostering distrust in AI if not managed properly.
The development of specialized AI 'stress-testing' and 'red-teaming' as an essential, high-value service will likely accelerate, becoming a critical part of the AI development and deployment ecosystem.
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Read at arXiv cs.AI