Same Patient, Different Words, Different Diagnosis? Evaluating Semantic Stability in Clinical LLMs
arXiv:2605.30646v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in clinical applications. However, their behavior remains highly sensitive to subtle linguistic variations, such as rephrasing or syntactic variation. This sensitivity poses risks in safety-critical healthcare settings, where semantically equivalent inputs should produce consistent predictions. However, a key challenge is to ensure that prompt variations truly preserve clinical meaning, as embedding-based similarity metrics often fail to capture distinctions involving negation, temporality, or
As LLMs become ubiquitous in critical applications like healthcare, robust evaluation of their real-world reliability and safety is paramount to their responsible deployment and public trust.
This research highlights a fundamental challenge in applying LLMs to high-stakes domains, where seemingly minor linguistic variations can have significant, adverse consequences, impacting patient safety and clinical decision-making.
The focus is shifting towards rigorous semantic stability testing for clinical LLMs, potentially leading to new standards for AI deployment in healthcare and influencing model development priorities.
- · AI safety researchers
- · Healthcare systems prioritizing AI ethics
- · Developers of robust, context-sensitive LLMs
- · Developers of unstable or poorly validated clinical LLMs
- · Healthcare providers relying on untested AI
- · Patients negatively impacted by inconsistent AI diagnoses
Clinical LLM development will increasingly incorporate semantic stability as a key performance metric.
New regulatory frameworks and certification processes will emerge to ensure the reliability and safety of AI in healthcare, particularly regarding linguistic robustness.
The demonstrated brittleness of current LLMs under linguistic variation may temper industry enthusiasm for rapid, unconstrained deployment in safety-critical sectors, fostering a more cautious and scientific approach.
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Read at arXiv cs.AI