A new commentary in the Journal of Medical Systems argues that today’s AI systems can achieve benchmark scores that look impressive, but the evidence often falls short of proving safety, effectiveness, and clinical appropriateness for real-world deployment. The piece frames “clinical readiness” claims as needing clearer validation pathways. The article points to gaps between performance on curated tests and performance in heterogeneous clinical environments, emphasizing that standardized claims should be supported by practical evaluation, not just competition-style metrics. For biotech and healthtech teams building clinical decision support, the piece reinforces the need for end-to-end evaluation plans that align model behavior with patient safety and workflow realities before scaling.