ArticleCommunications medicine2025
Current applications and challenges in large language models for patient care: a systematic review.
Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 170 papers, 6 of them syntheses 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
170 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Research on patient-facing chatbots based on large language models in the care of older people: a living systematic review.European geriatric medicine · 2026Pooled it
- Testing and evaluation of generative large language models in electronic health record applications: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- Digital affordances of AI chatbots in nursing education: a systematic review of learning gains and gaps in the evidence.Frontiers in medicine · 2026Pooled it
- Evaluating Large Language Models in Ophthalmology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Unveiling the Potential of Large Language Models in Transforming Chronic Disease Management: Mixed Methods Systematic Review.Journal of medical Internet research · 2025Pooled it
- A systematic review of large language model (LLM) evaluations in clinical medicine.BMC medical informatics and decision making · 2025Pooled 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
- Evaluation of a Retrieval-Augmented Generation-Powered Chatbot for Pre-CT Informed Consent: a Prospective Comparative Study.Journal of imaging informatics in medicine · 2025Trial
- Comparative Evaluation of AI Chatbots for Testicular Cancer Education: Validity, Information Quality, and Readability.Annals of surgical oncology · 2026Article
- User Beware: Inaccuracy and Inconsistency of Large Language Models in Providing Precision Dosing Recommendations for Patients With Kidney Impairment-A Case Series.Pharmacotherapy · 2026Article
- Large language models' performance in answering common patient questions about colonoscopy: an expert-based evaluation.BMC gastroenterology · 2026Observational
- Value in app store metadata and user reviews: A dual perspective on quality of Parkinson's and dementia apps.PLOS digital health · 2026Article
- A Clinician-Centered Evaluation Framework for Large Language Models in Patient Education: Integrating the Technology Acceptance Model and Medical Condition Regard Scale.JMIR formative research · 2026Article
- EmmaEmb: A quantitative framework for analyzing embedding spaces in molecular biology.Patterns (New York, N.Y.) · 2026Article
- Exploring Bias in Medical Applications of Large Language Models: Protocol for a Systematic Review.JMIR research protocols · 2026Article
- Large language models show high clinical safety but differences in completeness for reproductive counselling in women with inflammatory rheumatic and musculoskeletal diseases: a comparative expert evaluation.Rheumatology international · 2026Article
- Large Language Models for Distress Rating in Korean Psycho-Oncology Interviews: Exploratory Clinician-Benchmarked Evaluation Study.Journal of medical Internet research · 2026Article
- Large language models in neuroradiology: an international survey of awareness, applications, and concerns.Neuroradiology · 2026Article
110 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
14 authors.
Funding
Abstract
backgroundThe introduction of large language models (LLMs) into clinical practice promises to improve patient education and empowerment, thereby personalizing medical care and broadening access to medical knowledge. Despite the popularity of LLMs, there is a significant gap in systematized information on their use in patient care. Therefore, this systematic review aims to synthesize current applications and limitations of LLMs in patient care.
methodsWe systematically searched 5 databases for qualitative, quantitative, and mixed methods articles on LLMs in patient care published between 2022 and 2023. From 4349 initial records, 89 studies across 29 medical specialties were included. Quality assessment was performed using the Mixed Methods Appraisal Tool 2018. A data-driven convergent synthesis approach was applied for thematic syntheses of LLM applications and limitations using free line-by-line coding in Dedoose.
resultsWe show that most studies investigate Generative Pre-trained Transformers (GPT)-3.5 (53.2%, n = 66 of 124 different LLMs examined) and GPT-4 (26.6%, n = 33/124) in answering medical questions, followed by patient information generation, including medical text summarization or translation, and clinical documentation. Our analysis delineates two primary domains of LLM limitations: design and output. Design limitations include 6 second-order and 12 third-order codes, such as lack of medical domain optimization, data transparency, and accessibility issues, while output limitations include 9 second-order and 32 third-order codes, for example, non-reproducibility, non-comprehensiveness, incorrectness, unsafety, and bias.
conclusionsThis review systematically maps LLM applications and limitations in patient care, providing a foundational framework and taxonomy for their implementation and evaluation in healthcare settings.
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.