Retracted: The Role of Artificial Intelligence in Patient Recruitment and Optimization in Clinical Trials: Review of Literature and Future Directions
Article Sidebar
Main Article Content
Abstract
Clinical trials are considered the gold standard of medical evidence, serving as the primary method for evaluating the safety and effectiveness of new medical interventions before patient use. However, they are often lengthy, costly, and have relatively low success rates despite significant financial investment. In response to the challenges of cost, time, and efficiency, Artificial Intelligence (AI) is transforming clinical trial processes by analyzing large datasets, improving patient recruitment and trial matching, enabling automated patient monitoring, and enhancing the reliability and efficiency of endpoint assessment.
Article Details
Copyright (c) 2026 De Roxas R.
This work is licensed under a Creative Commons Attribution 4.0 International License.
ClinicalTrials.gov. Trends, charts, and maps [Internet]. U.S. National Library of Medicine; 2024 [cited 2025 Sep 27]. Available from: https://clinicaltrials.gov/ct2/resources/trends.
Wong CH, Siah KW, Lo AW. Estimation of clinical trial success rates and related parameters. Biostatistics. 2019;20(2):273-286. Available from: https://doi.org/10.1093/biostatistics/kxx069
Kelly D, Spreafico A, Siu LL. Increasing operational and scientific efficiency in clinical trials. Br J Cancer. 2020;123:1207-1208. Available from: https://www.nature.com/articles/s41416-020-0990-8
Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2. Available from: https://doi.org/10.1136/svn-2017-000101
Miller MI, Shih LC, Kolachalama VB. Machine learning in clinical trials: a primer with applications to neurology. Neurotherapeutics. 2023;20(4):1066-1080. Available from: https://doi.org/10.1007/s13311-023-01384-2
Chaudhari N, Ravi R, Gogtay NJ, Thatte UM. Recruitment and retention of the participants in clinical trials: challenges and solutions. Perspect Clin Res. 2020;11:64. Available from: https://doi.org/10.4103/picr.picr_206_19
Devlin J, Chang MW, Lee K, Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. arXiv [Preprint]. 2018. Available from: https://doi.org/10.48550/arXiv.1810.04805
Haddad T, et al. Accuracy of an artificial intelligence system for cancer clinical trial eligibility screening: retrospective pilot study. JMIR Med Inform. 2021;9. Available from: https://doi.org/10.2196/27767
Calaprice-Whitty D, Galil K, Salloum W, Zariv A, Jimenez B. Improving clinical trial participant prescreening with artificial intelligence (AI): a comparison of the results of AI-assisted vs standard methods in 3 oncology trials. Ther Innov Regul Sci. 2020;54(1):69-74. Available from: https://doi.org/10.1007/s43441-019-00030-4
Unlu O, Shin J, Mailly CJ, Oates MF, Tucci MR, Varugheese M, et al. Retrieval augmented generation enabled generative pre-trained transformer 4 (GPT-4) performance for clinical trial screening. medRxiv [Preprint]. 2024 Feb 8:2024.02.08.24302376. Available from: https://doi.org/10.1101/2024.02.08.24302376
Liu R, et al. Evaluating eligibility criteria of oncology trials using real-world data and AI. Nature. 2021;592:629-633. Available from: https://doi.org/10.1038/s41586-021-03430-5
Jreich R, Zhang H, Meng Z, Wang F. Evaluating the robustness of an AI pathfinder application on eligibility criteria in multiple myeloma trials using real-world data and historical trials. J Comp Eff Res. 2024;13(7). Available from: https://doi.org/10.57264/cer-2023-0164
Hernán MA, Robins JM. Using big data to emulate a target trial when a randomized trial is not available. Am J Epidemiol. 2016;183:758-764. Available from: https://doi.org/10.1093/aje/kwv254
Leiva V, Castro C. Artificial intelligence and blockchain in clinical trials: enhancing data governance efficiency, integrity, and transparency. Bioanalysis. 2025;17(3):161-176. Available from: https://doi.org/10.1080/17576180.2025.2452774
Beaulieu T, Knight R, Nolan S, Quick O, Ti L. Artificial intelligence interventions focused on opioid use disorders: a review of the gray literature. Am J Drug Alcohol Abuse. 2021;47:26-42. Available from: https://doi.org/10.1080/00952990.2020.1817466
Waltz E. Drugs go wireless. Nat Biotechnol. 2016;34:15-19. Available from: https://doi.org/10.1038/nbt.3446
Wyner Z, Dublin S, Chambers C, Deval S, Herzig-Marx C, Rao S, et al. The FDA MyStudies app: a reusable platform for distributed clinical trials and real-world evidence studies. JAMIA Open. 2020;3(4):500-505. Available from: https://doi.org/10.1093/jamiaopen/ooaa061
Benjamens S, Dhunnoo P, Meskó B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digit Med. 2020;3:118. Available from: https://doi.org/10.1038/s41746-020-00324-0
Aliper A, Kudrin R, Polykovskiy D, Kamya P, Tutubalina E, Chen S, et al. Prediction of clinical trials outcomes based on target choice and clinical trial design with multi-modal artificial intelligence. Clin Pharmacol Ther. 2023;114(5):972-980. Available from: https://doi.org/10.1002/cpt.3008
Lund BD, Wang T, Mannuru NR, Nie B, Shimray S, Wang Z. ChatGPT and a new academic reality: artificial intelligence-written research papers and the ethics of the large language models in scholarly publishing. J Assoc Inf Sci Technol. 2023;74(5):570-581. Available from: https://doi.org/10.1002/asi.24750
Hernandez AF, Lindsell CJ. The future of clinical trials: artificial to augmented to applied intelligence. JAMA. 2023;330(21):2061-2063. Available from: https://doi.org/10.1001/jama.2023.23822
Aggarwal R, Bhatt DL. The potential of artificial intelligence in clinical trials. Eur J Clin Invest. 2026;56(3). Available from: https://doi.org/10.1111/eci.70182
Han R, Acosta J, Shakeri Z, et al. Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review. Lancet Digit Health. 2024;6. Available from: https://doi.org/10.1016/s2589-7500(24)00047-5
Barucci A, Colcelli V, De Masi S, Falconi M, Carmela Leo M, Marzola A, et al. Ethical and regulatory frameworks for artificial intelligence in clinical research: a European perspective on the Artificial Intelligence Act for ethics committees and researchers. Eur Cardiol. 2026;21. Available from: https://doi.org/10.15420/ecr.2025.59