Moving beyond the benchmarks: Five foundational principles for meaningful AI evaluation in healthcare

Rapid integration of Large Language Models (LLMs) into healthcare has exposed a critical disconnect between technical performance and clinical value. While state-of-the-art models achieve impressive scores on standardized medical examinations, their real-world impact remains limited, with few models progressing to successful clinical integration. This disconnect persists, in part, due to a proliferation of evaluation practices that prioritize static, decontextualized benchmarks. To help address this gap, we propose five foundational principles to guide contextually appropriate evaluations of healthcare AI: Local (grounded in specific deployment contexts), Task-specific (aligned with intended clinical use), Agile (continuously adaptive), Reflective (acknowledging limitations and inherent value-sensitivity), and Community-partnered (centering affected voices). We argue that emphasis on these principles can help shift evaluation practice towards assessment of artificial intelligence. This reorientation is essential for developing healthcare AI that not only performs well technically, but also can meaningfully improve patient care, serve communities for defined purposes, and mitigate (rather than exacerbate) health disparities.