ReviewBJR open2023
The role of artificial intelligence in hastening time to recruitment in clinical trials.
Review in BJR open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions.Medical physics · 2026Review
- The Role of AI in Clinical Trial Design and Scientific Writing.Cardiovascular and interventional radiology · 2026Review
- A unified framework for pre-screening and screening tools in oncology clinical trials.NPJ precision oncology · 2026Review
- Artificial intelligence in clinical trial participant recruitment and retention: A scoping review and meta-analysis.Journal of clinical and translational science · 2026Review
- Large language models to infer depression in patients with neurological conditions.BMJ neurology open · 2026Article
- The Adoption and Use of Artificial Intelligence and Machine Learning in Clinical Development.Therapeutic innovation & regulatory science · 2025Article
- Enhanced pre-recruitment framework for clinical trial questionnaires through the integration of large language models and knowledge graphs.Scientific reports · 2025Article
- Article
- Artificial intelligence to revolutionize IBD clinical trials: a comprehensive review.Therapeutic advances in gastroenterology · 2025Review
- Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Challenges and opportunities in cancer immunotherapy: a Society for Immunotherapy of Cancer (SITC) strategic vision.Journal for immunotherapy of cancer · 2024Review
- Reducing Sample Size While Improving Equity in Vaccine Clinical Trials: A Machine Learning-Based Recruitment Methodology with Application to Improving Trials of Hepatitis C Virus Vaccines in People Who Inject Drugs.Healthcare (Basel, Switzerland) · 2024Article
- Application of Artificial Intelligence or machine learning in risk sharing agreements for pharmacotherapy risk management.Journal of integrative bioinformatics · 2023Article
- Artificial intelligence in maternal and child health: Current applications, translational gaps, and future research priorities.Women's health (London, England)Review
- The Rise of Generalist Foundation Models and Quantum Computing in Oncology.Technology in cancer research & treatmentReview
- [[Role of Artificial Intelligence in Clinical Research: MethodologicalApplications and Current Challenges]].Acta gastroenterologica LatinoamericanaArticle
- Exploring the current and future role of artificial intelligence in pharmaceutical market access: insights from a European survey.Global & regional health technology assessmentArticle
- Natural Language Processing in Clinical Research Recruitment: A Scoping Review Enriched with Stakeholder Insights.Ethics & human researchArticle
- Application of artificial intelligence in gynecologic cancers: A bibliometric analysis.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Novel and developing artificial intelligence (AI) systems can be integrated into healthcare settings in numerous ways. For example, in the case of automated image classification and natural language processing, AI systems are beginning to demonstrate near expert level performance in detecting abnormalities such as seizure activity. This paper, however, focuses on AI integration into clinical trials. During the clinical trial recruitment process, considerable labor and time is spent sifting through electronic health record and interviewing patients. With the advancement of deep learning techniques such as natural language processing, intricate electronic health record data can be efficiently processed. This provides utility to workflows such as recruitment for clinical trials. Studies are starting to show promise in shortening the time to recruitment and reducing workload for those involved in clinical trial design. Additionally, numerous guidelines are being constructed to encourage integration of AI into the healthcare setting with meaningful impact. The goal would be to improve the clinical trial process by reducing bias in patient composition, improving retention of participants, and lowering costs and labor.
Identifiers
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.