消费者健康中的生成式人工智能:利用数字健康框架利用大型语言模型促进健康素养和临床安全。

IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES
Frontiers in digital health Pub Date : 2025-08-26 eCollection Date: 2025-01-01 DOI:10.3389/fdgth.2025.1616488
Annemarie K Tilton, Brian E Caplan, Brian J Cole
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引用次数: 0

摘要

由大型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的健康状况。然而,确保临床安全和有效性需要强有力的数字卫生框架,以应对错误信息和不公平沟通等风险。这篇小型综述审查了目前在消费者健康教育中生成人工智能的用例,强调了持续存在的挑战,并提出了一个临床医生知情的框架来评估安全性、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——提供了一个务实的视角,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型语言模型的机遇和陷阱联系起来,本文为未来的发展提供了背景和方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Generative AI in consumer health: leveraging large language models for health literacy and clinical safety with a digital health framework.

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.

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CiteScore
4.20
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