ReviewNPJ artificial intelligence2025
Large language models for disease diagnosis: a scoping review.
Review in NPJ artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 2 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
55 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence in Cardiovascular Medicine: Focus on Hypertension.Hypertension (Dallas, Tex. : 1979) · 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
- Real-Time Artificial Intelligence Diagnostic Copilot in Simulated Primary Care Consultations: Randomized Simulation Study.JMIR formative research · 2026Trial
- Generative AI-enabled clinical decision support system in primary care: a pragmatic, cluster-randomized trial.Nature medicine · 2026Trial
- CURENet: combining unified representations for efficient chronic disease prediction.Health information science and systems · 2026Article
- Large language models in the diagnosis and treatment of immunosuppression-related infections in rheumatic diseases: a comparative, vignette-based study.Rheumatology international · 2026Article
- Large Language Models Meet Gynecologic Ultrasound: Advancing the Characterization of ADNEXal Masses.Journal of imaging · 2026Article
- On-premise medical AI agents for reliable clinical decision-making.Nature medicine · 2026Article
- Machine Learning, Large Language Models, and Multimodal AI for Diagnosing Pediatric Rare Diseases: Scoping Review.Journal of medical Internet research · 2026Article
- Conversational Large Language Models for Vestibular Diagnosis in Outpatient Clinics: Prospective Multicenter Diagnostic Accuracy Study.Journal of medical Internet research · 2026Article
- Tutorial: guidance on the use of large language models for medical research.Nature protocols · 2026Review
- Artificial intelligence in endodontic decision-making: hallucinations and emerging challenges for clinical practice.Acta odontologica Scandinavica · 2026Article
- Assessing AI and Neurologist Diagnostic Reasoning Against Neuropathological Ground Truth.medRxiv : the preprint server for health sciences · 2026Article
- Large Language Models in Sensor-Driven Control Systems: Architectures, Challenges, and Opportunities.Sensors (Basel, Switzerland) · 2026Review
- Symptom-Only Localization of Brainstem Ischemia Using Large Language Models Versus Neurologists in Diffusion-Weighted Imaging-Positive Cases: Retrospective Single-Center Study.JMIR formative research · 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
- Memorization in large language models in medicine prevalence characteristics and implications.Nature communications · 2026Article
- Diagnostic Performance of Contemporary Large Language Models on Free-Text Histopathologic Descriptions in Oral and Maxillofacial Pathology.Head and neck pathology · 2026Article
- Explainable AI for mental health emergency returns: integrating large language models with predictive modeling.JAMIA open · 2026Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intelligence, with growing evidence supporting the efficacy of LLMs in diagnostic tasks. Despite the increasing attention in this field, a holistic view is still lacking. Many critical aspects remain unclear, such as the diseases and clinical data to which LLMs have been applied, the LLM techniques employed, and the evaluation methods used. In this article, we perform a comprehensive review of LLM-based methods for disease diagnosis. Our review examines the existing literature across various dimensions, including disease types and associated clinical specialties, clinical data, LLM techniques, and evaluation methods. Additionally, we offer recommendations for applying and evaluating LLMs for diagnostic tasks. Furthermore, we assess the limitations of current research and discuss future directions. To our knowledge, this is the first comprehensive review for LLM-based disease diagnosis.
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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.