ReviewPatient preference and adherence2025
Generative AI/LLMs for Plain Language Medical Information for Patients, Caregivers and General Public: Opportunities, Risks and Ethics.
Review in Patient preference and adherence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 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
- Readability, Quality, Understandability, and Actionability of ChatGPT Generated GI Patient Education Versus AGA Patient Center.Digestive diseases and sciences · 2026Article
- Do Androids Dream of Lived Experience? Navigating Risks, Uncertainty, and Best Practices Around Generative AI in Patient and Public Involvement and Engagement.Journal of medical Internet research · 2026Article
- GPT-4 improves sex-specificity in cardiovascular patient education but may perpetuate gender biases: A mixed-methods audit.PLOS digital health · 2026Article
- Comparative Performance of AI Models and Clinicians in Evidence-Based Cardiovascular Disease Management for People Living With HIV: Comparative Study.Journal of medical Internet research · 2026Article
- A Qualitative Study Evaluating the Parent-Specific Cow's Milk-Related Symptom Score (Pre-CoMiSS™).Nutrients · 2026Article
- Explainable and Trustworthy Artificial Intelligence in Cardiology: A Narrative Review of Clinical Applications, Operational Integration, and Future Directions.Journal of clinical medicine · 2026Review
- Online patient education resources in bariatric surgery: a systematic evaluation of quality, readability, transparency, and representation.Surgical endoscopy · 2026Article
- "ChatGPT knows my Parkinson's": Perspectives of people with Parkinson's disease on use of generative AI.Journal of Parkinson's disease · 2026Article
- Article
- Healthcare Goes Digital: mHealth, eHealth, Artificial Intelligence, and Emerging Digital Technologies Within Digital Health Transformation.Healthcare (Basel, Switzerland) · 2026Article
- Development and comparative evaluation of knowledge graph-enhanced large language models for domain-specific question answering in nursing.BMC nursing · 2026Article
- Article
- A systematic review of the limitations of large language models in generating healthcare content.PLOS digital health · 2026Article
- [Artificial intelligence and disinformation in health: The need for re-education from primary care].Atencion primaria · 2026Review
- Helping people navigate and make sense of cancer information: challenges and perspectives.Future oncology (London, England) · 2026Article
- AI Health Literacy: a reflective framework for LLM-based generative AI in public health education and promotion.Frontiers in public health · 2026Article
- Validation of an AI-powered mobile application for personalizing medical note explanations: a mixed-methods evaluation.Frontiers in digital health · 2026Article
- Parents' Perspectives on Artificial Intelligence-Supported Pediatric Healthcare Services: A Descriptive and Correlational Study.Journal of nursing management · 2026Article
- Preparedness for generative AI adoption among Chinese cancer survivors: a multi-center cross-sectional survey study.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative artificial intelligence (gAI) tools and large language models (LLMs) are gaining popularity among non-specialist audiences (patients, caregivers, and the general public) as a source of plain language medical information. AI-based models have the potential to act as a convenient, customizable and easy-to-access source of information that can improve patients' self-care and health literacy and enable greater engagement with clinicians. However, serious negative outcomes could occur if these tools fail to provide reliable, relevant and understandable medical information. Herein, we review published findings on opportunities and risks associated with such use of gAI/LLMs. We reviewed 44 articles published between January 2023 and July 2024. From the included articles, we find a focus on readability and accuracy; however, only three studies involved actual patients. Responses were reported to be reasonably accurate and sufficiently readable and detailed. The most commonly reported risks were oversimplification, over-generalization, lower accuracy in response to complex questions, and lack of transparency regarding information sources. There are ethical concerns that overreliance/unsupervised reliance on gAI/LLMs could lead to the "humanizing" of these models and pose a risk to patient health equity, inclusiveness and data privacy. For these technologies to be truly transformative, they must become more transparent, have appropriate governance and monitoring, and incorporate feedback from healthcare professionals (HCPs), patients, and other experts. Uptake of these technologies will also need education and awareness among non-specialist audiences around their optimal use as sources of plain language medical information.
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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.