ReviewInternational journal of medical sciences2025
Large Language Models in Medicine: Applications, Challenges, and Future Directions.
Review in International journal of medical sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 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
57 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
- Performance of Reasoning and Comparator Large Language Models on Nephrology Multiple-choice Questions.JMA journal · 2026Article
- Performance and Consistency of Large Language Models in Key Labor-Intensive Tasks of Systematic Reviews.Journal of evaluation in clinical practice · 2026Article
- Neuro-symbolic artificial intelligence in medicine.Nature biomedical engineering · 2026Article
- An automatic consult reply system for therapeutic plasma exchange using retrieval-augmented generation.Vox sanguinis · 2026Article
- Evaluating Large Reasoning Models Versus Human Multidisciplinary Teams in Lung Cancer Decision-Making: Real-World Study.Journal of medical Internet research · 2026Article
- Performance evaluation of domain-specific and general-purpose AI models for chest radiograph interpretation: a comparative study.BMC medical imaging · 2026Article
- Diagnostic Performance and Workup Efficiency of Large Language Models in Secondary Hypertension: A Blinded Comparative Study.Diagnostics (Basel, Switzerland) · 2026Article
- Applications, Challenges, and Future Directions of Large Language Models in Health Care Communication: Scoping Review.Journal of medical Internet research · 2026Article
- Automated PROMISE V2 Scoring from PSMA PET/CT Reports Using Large Language Models: A Comparative Evaluation of Prompt Design and Model Performance.Current oncology (Toronto, Ont.) · 2026Article
- Performance Evaluation of GPT-5, Grok 4, and DeepSeek R1 in Interpreting Complete Blood Count Reports for Hematologic Diseases: Retrospective Comparative Study.Journal of medical Internet research · 2026Article
- Performance comparison of a neuro-symbolic large language model system versus conventional AI models and human experts in cholangitis management.BMC medical informatics and decision making · 2026Article
- Diagnostic Performance of a Large Language Model (ChatGPT-4o) in Chronic Rhinosinusitis CT Scan Interpretation.Laryngoscope investigative otolaryngology · 2026Article
- Evaluation of ChatGPT-4o in oral and maxillofacial surgery examinations: a comparative study of performance on U.S. dental decks and chinese dental licensing examination practice questions.BMC oral health · 2026Article
- Benchmarking large language models on persian surgical subspecialty board examinations: a comparative study of ChatGPT-4o, ChatGPT-5, and Gemini 2.5 Flash.Scientific reports · 2026Article
- Improving self-care behaviors in patients with chronic obstructive pulmonary disease using a knowledge-based intelligent self-care system: protocol for a randomized controlled trial.Journal of thoracic disease · 2026Article
- Development and prospective shadow evaluation of a domain-specific large language model for emergency neurological diagnosis.NPJ digital medicine · 2026Article
- ChatHTN: a consultation model for hypertension.Scientific reports · 2026Article
- Article
- Benchmarking agreement between large language models and published clinical trial conclusions across four artificial intelligence platforms.Scientific reports · 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, large language models (LLMs) represented by GPT-4 have developed rapidly and performed well in various natural language processing tasks, showing great potential and transformative impact. The medical field, due to its vast data information as well as complex diagnostic and treatment processes, is undoubtedly one of the most promising areas for the application of LLMs. At present, LLMs has been gradually implemented in clinical practice, medical research, and medical education. However, in practical applications, medical LLMs still face numerous challenges, including the phenomenon of hallucination, interpretability, and ethical concerns. Therefore, in-depth exploration is still needed in areas of standardized evaluation frameworks, multimodal LLMs, and multidisciplinary collaboration in the future, so as to realize the widespread application of medical LLMs and promote the development and transformation in the field of global healthcare. This review offers a comprehensive overview of applications, challenges, and future directions of LLMs in medicine, providing new insights for the sustained development of medical LLMs.
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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.