ReviewBMC medicine2025
Large language models in clinical trials: applications, technical advances, and future directions.
Review in BMC medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
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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
24 citing papers in PubMed.
- How to apply artificial intelligence (AI) to facilitate and enhance MASH trials.Hepatology international · 2026Review
- From Invisible to Enrolled: How Artificial Intelligence Is Reshaping Clinical Trial Recruitment.Pharmaceutical medicine · 2026Article
- Large Language Models in Preclinical Spine Research: A Scoping Review and Expert Perspective on Evidence-Aware Experimental Workflows.JOR spine · 2026Article
- DFGLM-TCM: an integrated knowledge-and experience-driven large language model system for Traditional Chinese Medicine practice.Chinese medicine · 2026Article
- Artificial Intelligence Readiness in Clinical Trial Operations: A Narrative Review and Site-Level Governance Framework.Healthcare (Basel, Switzerland) · 2026Review
- Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions.Medical physics · 2026Review
- Retrieval-Augmented Large Language Model Counseling for Continuous Glucose Monitoring in Diabetes: Source-Masked Multirater Comparative Evaluation.Journal of medical Internet research · 2026Article
- Perceptions of challenges in clinical pediatric research: a survey of Italian pediatricians.Italian journal of pediatrics · 2026Article
- Anoctamin 5 as a protective factor in prostate cancer: Insights from WGCNA, machine learning, and experimental analysis, with a focus on the anoctamin family.Translational oncology · 2026Article
- Identification of MYC co-expression gene: POLR3G is associated with cell senescence, immunotherapy, chemotherapy responses, and clinical prognosis in bladder cancer patients.Translational oncology · 2026Article
- Medicine digital transformation: evidence from Chinese physicians on generative artificial intelligence implementation and challenges.Journal of translational medicine · 2026Article
- Retrieval-augmented generation-enhanced large language models for comprehensive CAD-RADS 2.0 categorization from structured coronary CTA reports.Radiologie (Heidelberg, Germany) · 2026Article
- Transformer-based relation extraction and concept normalization using an annotated clinical trials corpus.Scientific data · 2026Article
- Molecular Subtyping Based on EGFR Mutation-Associated Genes and the Prognostic Role of TRAF2 in Lung Adenocarcinoma.Human mutation · 2026Article
- Clinical Application Progress of Artificial Intelligence in Pancreatic Cancer: From Diagnosis to Immunotherapy.Oncology research · 2026Review
- Heart-liver co-management: epidemiology, mechanisms, and novel therapeutic approaches for metabolic dysfunction-associated steatotic liver disease and coronary artery disease.Frontiers in cardiovascular medicine · 2026Review
- Retrieval-enhanced drafting of ClinicalTrials.gov data elements from clinical protocols.Journal of clinical and translational science · 2026Article
- From Germline Susceptibility to Therapeutic Vulnerability: DNA Damage Response Gene Mutations Driving Multiple Myeloma Evolution and Precision Therapy.Human mutation · 2026Review
- AI at the Sella Turcica: Multi-Model Large Language Model Evaluation in Pituitary Adenomas.Brain & spine · 2026Article
- Large language models in healthcare quality management: a European perspective on process automation and compliance.Frontiers in digital health · 2026Review
Corrections and comments
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Authors and funding
21 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAs clinical trials scale up and grow more complex, researchers are facing mounting challenges, including inefficient participant recruitment, complex data management, and limited risk monitoring. These issues not only increase the workload for clinical researchers but also compromise trial reliability and safety, potentially elevating the risk of trial failure. Large language models (LLMs), as an emerging technology in natural language processing (NLP), exhibit notable advantages across various tasks, such as information extraction and relation classification. MAIN TEXT: With domain-specific pre-training and fine-tuning, LLMs present promising potential in clinical trial tasks such as automated patient-trial matching and the extraction and processing of trial data, which are anticipated to reduce time and financial costs. Additionally, they offer valuable insights for scientific rationale, medical decision-making, and trial endpoint prediction. In this context, an increasing number of studies have begun to explore the applications of LLMs in the design and conduct of clinical trials.
conclusionThis paper provides a review of LLM applications in clinical trials with an emphasis on real-world integration. Comparative advantages over traditional NLP models, technical limitations, and future implementation challenges are also discussed. This narrative review aims to highlight the potential of LLMs in clinical trial workflows and clarify key challenges and future directions.
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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.