Beyond Performance Benchmarks

This chapter, per the authors, provides a comprehensive evaluation of Large Language Models (LLMs) within safety-critical healthcare environments, moving beyond traditional performance benchmarks toward a reliability-centered paradigm. The purpose of this chapter is to identify core clinical risks—including hallucinations, bias, uncertainty mismanagement, and privacy leaks—that can compromise patient safety despite high accuracy on standardized tests. Systematic principles for safety-critical assessment are established, emphasizing robustness, uncertainty awareness, and human-in-the-loop validation as essential for clinical trust. Furthermore, the authors discuss technical methods for stress testing and bias mitigation alongside the necessity of continuous monitoring, auditability, and lifecycle evaluation to handle model drift. By aligning these technical frameworks with emerging international regulatory standards, the chapter outlines a path for integrating clinical language models as dependable medical aids.