MINI REVIEW article
Front. Digit. Health
Sec. Ethical Digital Health
Volume 7 - 2025 | doi: 10.3389/fdgth.2025.1616488
This article is part of the Research TopicNavigating Digital Health: Balancing Innovation, Safety, and Regulatory ChallengesView all 4 articles
Generative AI in Consumer Health: Leveraging Large Language Models for Health Literacy and Clinical Safety with a Digital Health Framework
Provisionally accepted
- 1Independant Researcher, Park City, UT, United States
- 2Rush University Medical Center, Chicago, Illinois, United States
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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-Relevance, Evidence-based, Clarity, Adaptability, and Precision-offers a pragmatic lens to guide responsible implementation of AI in patient-facing tools. By connecting insights from past digital health innovations to the opportunities and pitfalls of large language models, this paper provides both context and direction for future development.
Keywords: Generative AI, Large Language Models (LLMs), Consumer health education, Health Literacy, clinical safety, AI Evaluation Framework, Digital Health Ethics
Received: 22 Apr 2025; Accepted: 12 Aug 2025.
Copyright: © 2025 Tilton, Caplan and Cole. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Annemarie Tilton, Independant Researcher, Park City, UT, United States
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.