Measuring What Matters: Benchmarking Generative, Multimodal, and Agentic AI in Healthcare

A new academic paper submitted to arXiv addresses the critical gap in evaluating artificial intelligence models deployed in live clinical environments. The authors argue that current benchmarks, which often yield near-perfect scores on medical licensing exams, fail to capture the complexity, reliability, and safety required for real-world healthcare workflows. While frontier AI models excel in theoretical knowledge tests, their performance degrades significantly when applied to practical tasks such as documentation, clinical decision support, and administrative workflows. The study highlights that existing evaluation methods provide a false sense of deployment readiness, as they do not systematically measure clinical relevance or failure rates under high-stakes conditions. The paper calls for a principled framework for benchmark design that moves beyond ad hoc dataset construction. By establishing structured combinations of tasks, datasets, and metrics, the field can better determine whether poor clinical performance stems from model limitations or flawed measurement strategies. This research is essential for ensuring that generative, multimodal, and agentic AI systems are safe and effective before assuming consequential roles in patient care.