ReviewTranslational and clinical pharmacology2023
Transforming clinical trials: the emerging roles of large language models.
Review in Translational and clinical pharmacology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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
28 citing papers in PubMed.
- Leveraging electronic health records for atrial fibrillation cohort generation.Health information science and systems · 2026Article
- From Invisible to Enrolled: How Artificial Intelligence Is Reshaping Clinical Trial Recruitment.Pharmaceutical medicine · 2026Article
- AI-driven drug design: a comprehensive review.Journal of computer-aided molecular design · 2026Review
- Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions.Medical physics · 2026Review
- Artificial Intelligence in Rhinology: A State-of-the-Art Review of Clinical Readiness and Implementation Pathways.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026Review
- DiscoVerse: multi-agent pharmaceutical co-scientist for traceable drug discovery and reverse translation.Frontiers in artificial intelligence · 2026Article
- Article
- Towards Efficient Patient Recruitment for Clinical Trials: Application of a Prompt-Based Learning Model.Healthcare informatics research · 2025Article
- Large Language Models in Randomized Controlled Trials Design: Observational Study.Journal of medical Internet research · 2025Observational
- A scoping review of artificial intelligence applications in clinical trial risk assessment.NPJ digital medicine · 2025Article
- The Emergence of AI in Public Health Is Calling for Operational Ethics to Foster Responsible Uses.International journal of environmental research and public health · 2025Article
- Large Language Models in Medicine: Clinical Applications, Technical Challenges, and Ethical Considerations.Healthcare informatics research · 2025Article
- Exploration of Using an Open-Source Large Language Model for Analyzing Trial Information: A Case Study of Clinical Trials With Decentralized Elements.Clinical and translational science · 2025Article
- A Large Language Model-Based Approach for Coding Information from Free-Text Reported in Fall Risk Surveillance Systems: New Opportunities for In-Hospital Risk Management.Journal of clinical medicine · 2025Article
- Transforming literature screening: The emerging role of large language models in systematic reviews.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Engineering of Generative Artificial Intelligence and Natural Language Processing Models to Accurately Identify Arrhythmia Recurrence.Circulation. Arrhythmia and electrophysiology · 2025Article
- Large language model usage guidelines in Korean medical journals: a survey using human-artificial intelligence collaboration.Journal of Yeungnam medical science · 2025Article
- Guidelines From the American Society of Pain and Neuroscience for Using Artificial Intelligence in Interventional Spine and Nerve Treatment.Journal of pain research · 2025Article
- Large language models in cancer: potentials, risks, and safeguards.BJR artificial intelligence · 2025Review
- Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis.Journal of healthcare informatics research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clinical trials are essential for medical research, but they often face challenges in matching patients to trials and planning. Large language models (LLMs) offer a promising solution, signaling a transformative shift in the field of clinical trials. This review explores the multifaceted applications of LLMs within clinical trials, focusing on five main areas expected to be implemented in the near future: enhancing patient-trial matching, streamlining clinical trial planning, analyzing free text narratives for coding and classification, assisting in technical writing tasks, and providing cognizant consent via LLM-powered chatbots. While the application of LLMs is promising, it poses challenges such as accuracy validation and legal concerns. The convergence of LLMs with clinical trials has the potential to revolutionize the efficiency of clinical trials, paving the way for innovative methodologies and enhancing patient engagement. However, this development requires careful consideration and investment to overcome potential hurdles.
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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.