ArticleFrontiers in medicine2024
Large language models in patient education: a scoping review of applications in medicine.
Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 153 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
153 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Large language models for simplifying radiology reports: a systematic review and meta-analysis of patient, public, and clinician evaluations.The Lancet. Digital health · 2026Pooled it
- Global trends and thematic clusters in healthcare professional education, medical errors, and patient safety: a bibliometric analysis, 2000-2026.Frontiers in medicine · 2026Pooled it
- Rethinking Pediatric Asthma Education Through Large Language Model Generation and Simplification: Randomized Double-Blind Study.Journal of medical Internet research · 2026Trial
- Effectiveness of Al-Assisted Patient Health Education Using Voice Cloning and ChatGPT: Prospective Randomized Controlled Trial.Journal of medical Internet research · 2026Trial
- 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
- Evaluating the impact of AI-generated educational content on patient understanding and anxiety in endodontics and restorative dentistry: a comparative study.BMC oral health · 2025Trial
- ChatGPT Health and ChatGPT Plus in Urogynecology: A Blinded Comparison of Response Quality, Guideline Concordance, and Safety.International urogynecology journal · 2026Article
- Comparative Evaluation of AI Chatbots for Testicular Cancer Education: Validity, Information Quality, and Readability.Annals of surgical oncology · 2026Article
- Health literacy in perioperative care: toward better shared decision-making through navigation and communication design.Journal of anesthesia · 2026Review
- Article
- Evaluating a Guideline-Integrated Clinical Interaction Framework Vs a Standard Large Language Model Interaction for Dietary Recommendations in Recurrent Urolithiasis: In Silico Study.Journal of medical Internet research · 2026Article
- Evaluating the Accuracy, Empathy, and Readability of Generative AI Versus Registered Nurses in Discharge Planning: A Vignette-Based Study.Nursing open · 2026Article
- GPT-4 improves sex-specificity in cardiovascular patient education but may perpetuate gender biases: A mixed-methods audit.PLOS digital health · 2026Article
- Exploring Real-World Use of AI Chatbots for Mental Health Support: Cross-Sectional Survey Study.JMIR mental health · 2026Article
- The Impact of Specific Prompt Engineering Techniques on the Readability of LLM-Generated Patient Materials in Gastroenterology and Hepatology.Digestive diseases and sciences · 2026Article
- Large Language Models in Spine Surgery : A Narrative Review of Performance Paradox and Clinical Integration Challenges.Journal of Korean Neurosurgical Society · 2026Article
- Performance of large language models as a source of clinical information on bacteriophage therapy.Npj viruses · 2026Article
- Large Language Models for Patient Education in Cardiovascular Imaging: Prospective Observational Comparative Study.JMIR formative research · 2026Observational
- Evaluating ChatGPT's Effectiveness for Arabic Dry Mouth Patient Education.Healthcare (Basel, Switzerland) · 2026Article
- Comparison of responses from google and large language models to the top frequently asked questions on lumbar spinal stenosis: an evaluation of accuracy and completeness.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
93 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Large Language Models (LLMs) are sophisticated algorithms that analyze and generate vast amounts of textual data, mimicking human communication. Notable LLMs include GPT-4o by Open AI, Claude 3.5 Sonnet by Anthropic, and Gemini by Google. This scoping review aims to synthesize the current applications and potential uses of LLMs in patient education and engagement. Materials and methods: Following the PRISMA-ScR checklist and methodologies by Arksey, O'Malley, and Levac, we conducted a scoping review. We searched PubMed in June 2024, using keywords and MeSH terms related to LLMs and patient education. Two authors conducted the initial screening, and discrepancies were resolved by consensus. We employed thematic analysis to address our primary research question. Results: The review identified 201 studies, predominantly from the United States (58.2%). Six themes emerged: generating patient education materials, interpreting medical information, providing lifestyle recommendations, supporting customized medication use, offering perioperative care instructions, and optimizing doctor-patient interaction. LLMs were found to provide accurate responses to patient queries, enhance existing educational materials, and translate medical information into patient-friendly language. However, challenges such as readability, accuracy, and potential biases were noted. Discussion: LLMs demonstrate significant potential in patient education and engagement by creating accessible educational materials, interpreting complex medical information, and enhancing communication between patients and healthcare providers. Nonetheless, issues related to the accuracy and readability of LLM-generated content, as well as ethical concerns, require further research and development. Future studies should focus on improving LLMs and ensuring content reliability while addressing ethical considerations.
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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.