{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T17:36:04Z","timestamp":1772300164537,"version":"3.50.1"},"reference-count":20,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T00:00:00Z","timestamp":1756166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:p>Generative AI, powered by large language models, is transforming consumer health by enhancing health literacy and delivering personalized health education. However, ensuring clinical safety and effectiveness requires a robust digital health framework to address risks like misinformation and inequitable communication. This mini review examines current use cases for generative AI in consumer health education, highlights persistent challenges, and proposes a clinician-informed framework to evaluate safety, usability, and effectiveness. The RECAP model\u2014Relevance, Evidence-based, Clarity, Adaptability, and Precision\u2014offers a pragmatic lens to guide responsible implementation of AI in patient-facing tools. 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