Benchmarking Generative and Multimodal AI for Clinical Reliability

A new paper on arXiv (2605.08445) argues that existing benchmarks for healthcare AI fail to measure reliability, safety, and clinical relevance in real-world conditions. Current tests, often built from ad hoc datasets, focus on narrow task performance—frontier models achieve near-perfect scores on medical licensing exams but are not evaluated across the full complexity of clinical workflows. The authors call for systematic benchmarks combining tasks, datasets, and metrics to assess generative, multimodal, and agentic AI in live clinical environments.