ReviewFrontiers in digital health2025
Generative AI in consumer health: leveraging large language models for health literacy and clinical safety with a digital health framework.
Review in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of Large Language Models in Chronic Disease Care: Mixed Methods Systematic Review and Thematic Synthesis.Journal of medical Internet research · 2026Pooled it
- Evaluating and Validating Large Language Models for Health Education on Developmental Dysplasia of the Hip: 2-Phase Study With Expert Ratings and a Pilot Randomized Controlled Trial.Journal of medical Internet research · 2026Trial
- Exploring needs and priorities in digital health management for rare disease patients and their caregivers: A mixed-methods study.PLOS digital health · 2026Article
- The Reliability of Human Evaluation of Large Language Models in Health Care Settings: Scoping Review.Journal of medical Internet research · 2026Article
- Personal Health Large Language Models and the Negotiation of Medical Authority in Clinical Care: Opportunities, Risks, and Governance.Journal of medical Internet research · 2026Article
- Role-based evaluation of artificial intelligence chatbot responses in orthodontic emergency scenarios: accuracy, readability, understandability, and patient-oriented communication.BMC oral health · 2026Article
- From voice to value: listening, believing, empowering, and partnering with patients in rheumatology.Clinical rheumatology · 2026Article
- AI Health Literacy: a reflective framework for LLM-based generative AI in public health education and promotion.Frontiers in public health · 2026Article
- Performance evaluation of large language models in bladder cancer patient education Q&A: a cross-sectional study.Frontiers in oncology · 2026Article
- Psychological factors associated with Chinese users' adoption of AI-generated health education short videos: a PLS-SEM-ANN-IPMA study.Frontiers in psychology · 2026Article
- Preparedness for generative AI adoption among Chinese cancer survivors: a multi-center cross-sectional survey study.Frontiers in public health · 2026Article
- From information literacy to health literacy: AI-driven transformation in university libraries under digital public health-a perspective.Frontiers in public health · 2026Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.