Tilton Annemarie K, Caplan Brian E, Cole Brian J
Independent Researcher, Park City, UT, United States.
Department of Orthopaedics, Rush University Medical Center, Chicago, IL, United States.
Front Digit Health. 2025 Aug 26;7:1616488. doi: 10.3389/fdgth.2025.1616488. eCollection 2025.
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.
由大语言模型驱动的生成式人工智能正在通过提高健康素养和提供个性化健康教育来改变消费者健康状况。然而，要确保临床安全性和有效性，就需要一个强大的数字健康框架来应对错误信息和沟通不平等之类的风险。本微型综述探讨了生成式人工智能在消费者健康教育中的当前应用案例，突出了持续存在的挑战，并提出了一个由临床医生提供信息的框架，以评估安全性、可用性和有效性。RECAP模型（相关性、循证性、清晰度、适应性和精确性）提供了一个实用视角，以指导在面向患者的工具中负责任地实施人工智能。通过将过去数字健康创新的见解与大语言模型的机遇和陷阱联系起来，本文为未来发展提供了背景和方向。
