A new analysis in Nature Medicine raised a caution flag for healthcare AI tools: accuracy does not automatically translate into better patient outcomes. The paper highlighted that many widely deployed systems, including ambient AI scribes that transcribe and summarize clinical encounters, have been evaluated more for clinician or patient satisfaction than for downstream effects on clinical decision-making. The report comes as AI adoption accelerates across hospitals, increasing pressure for prospective evidence linking model use to clinical endpoints. For operators and AI vendors, it reframes validation needs toward workflow integration and measurable health outcomes. Separately, Merck KGaA executives argued at industry meetings that companies should map end-to-end workflows to where AI can make measurable impacts, rather than relying on hype. The message reinforces that ROI and regulatory-grade evaluation remain central challenges as healthcare AI proliferates.