arXiv:2606.07237v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in healthcare for tasks such as clinical question answering, diagnosis support, and report summarization. Despite their promise, these models remain highly sensitive to subtle prompt perturbations, both lexical and syntactic, posing serious risks in safety-critical clinical applications. In this study, we conduct a systematic sensitivity analysis to evaluate the robustness of both general-purpose (e.g., GPT-3.5, Llama3) and medical-specific LLMs (e.g., ClinicalBERT, BioLlama3, BioBERT) using th
This evaluation is timely as LLM adoption in critical sectors like healthcare is rapidly increasing, necessitating robust safety and reliability assessments.
The high sensitivity of LLMs to prompt variations poses significant risks in safety-critical clinical applications, highlighting a crucial barrier to widespread, trustworthy AI deployment in healthcare.
The focus of LLM development and deployment in healthcare will likely shift towards greater emphasis on robustness, explainability, and prompt engineering best practices rather than solely on performance metrics.
- · AI safety researchers
- · Prompt engineering specialists
- · Healthcare organizations with robust validation protocols
- · LLM developers prioritizing raw performance over robustness
- · Healthcare providers relying on untested LLM applications
- · Patients exposed to AI-driven diagnostic or treatment errors
Increased scrutiny and demand for more robust and less sensitive large language models in critical applications.
Development of specialized tools and methodologies for prompt robustness testing and mitigation strategies within AI development lifecycles.
Potential for regulatory bodies to mandate specific robustness standards for AI models used in safety-critical sectors like healthcare, impacting market entry for less robust models.
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Read at arXiv cs.LG