| Sumario: | The article focuses on the challenges of interpreting medical artificial intelligence (AI) benchmark results as evidence of clinical readiness. It distinguishes between the AI model being tested, the benchmark environment, and the clinical claims derived from benchmark scores, highlighting the risk of "benchmark claim inflation," where benchmark performance is overstated as deployment readiness without sufficient local validation, human factors evaluation, or workflow integration. To address this, the authors propose a "benchmark claim card," a governance tool that documents the scope, limitations, and evidentiary boundaries of benchmark-derived claims to prevent overclaiming and support responsible institutional review before procurement or deployment. The article emphasizes that while benchmarks are essential for controlled model comparison, they do not substitute for real-world evidence needed to ensure safe and effective clinical use.
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