ReviewHealth care science2023
Large language models in health care: Development, applications, and challenges.
Review in Health care science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 126 papers, 4 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
126 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Computational paradigms for antimicrobial resistance prediction: integrating multi-omics, structural modeling, and foundation artificial intelligence systems.Briefings in bioinformatics · 2026Pooled it
- The use of artificial intelligence based modelling techniques in One Health-related infectious disease studies in Sub-Saharan Africa: a review.Frontiers in artificial intelligence · 2026Pooled it
- Artificial intelligence for affective-domain development in healthcare professions education: a systematic review.Frontiers in medicine · 2026Pooled it
- The performance of large language models in dentomaxillofacial radiology: a systematic review.Dento maxillo facial radiology · 2025Pooled it
- Application of ChatGPT-assisted problem-based learning teaching method in clinical medical education.BMC medical education · 2025Trial
- Article
- The Alignment Paradox of Medical Large Language Models in Infertility Care: Decoupling Algorithmic Improvement From Clinical Decision-Making Quality.Journal of medical Internet research · 2026Article
- "Small" Large Language Models in the Hospital: Evaluation Study on Real-World Data in a Resource-Constrained Setting.Journal of medical Internet research · 2026Article
- Comparative performance of large language models for appraising bias in real-world evidence studies.Journal of managed care & specialty pharmacy · 2026Article
- Locally deployed context-aware chatbot outperforms generic large language models for guideline-concordant pediatric imaging recommendations.Pediatric radiology · 2026Article
- An Acceptance Criteria Framework for Determining the Implementation Fit of Custom Large Language Models in Public Health Interventions.Journal of medical Internet research · 2026Article
- Behavior Change Content and Implementation of Large Language Model-Driven Conversational Agents in Cardiometabolic Care: Scoping Review.Journal of medical Internet research · 2026Article
- Exploring the Role of Large Language Models in Primary Care: Qualitative Study of Physicians in the United States and the Netherlands.JMIR medical informatics · 2026Article
- Comparative Evaluation of Large Language Models for Reporting Jaw Lesions on Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Spine patient history-taking: a two phase study of a prompt-engineered large language model assistant.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
- Large Language Models for Diagnosis and Prognosis of Chronic Liver Diseases: A Systematic Review.Health science reports · 2026Review
- Multimodal Integration of Ambulatory ECG and Clinical Features for Sudden Cardiac Death and Pump Failure Death PredictionmedRxiv : the preprint server for health sciences · 2026Article
- Distinguishing cancer patients' mild and severe symptoms in radiotherapy via zero-shot and few-shot large language model-based probabilistic prompts.BioData mining · 2026Article
- Unlikely Storyteller: Leveraging Narrative-Based Communication in LLM-Generated Medical Advice.Healthcare (Basel, Switzerland) · 2026Article
- Generalist large language models in a specialized world: Evidence from the Italian national medical education pathway.PLOS digital health · 2026Article
66 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, the emergence of ChatGPT, an artificial intelligence chatbot developed by OpenAI, has attracted significant attention due to its exceptional language comprehension and content generation capabilities, highlighting the immense potential of large language models (LLMs). LLMs have become a burgeoning hotspot across many fields, including health care. Within health care, LLMs may be classified into LLMs for the biomedical domain and LLMs for the clinical domain based on the corpora used for pre-training. In the last 3 years, these domain-specific LLMs have demonstrated exceptional performance on multiple natural language processing tasks, surpassing the performance of general LLMs as well. This not only emphasizes the significance of developing dedicated LLMs for the specific domains, but also raises expectations for their applications in health care. We believe that LLMs may be used widely in preconsultation, diagnosis, and management, with appropriate development and supervision. Additionally, LLMs hold tremendous promise in assisting with medical education, medical writing and other related applications. Likewise, health care systems must recognize and address the challenges posed by LLMs.
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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.