Artificial Intelligence for Optimizing Recruitment and Retention in Clinical Trials: A Scoping Review
Open accessAbstract
OBJECTIVE: The objective of our research is to conduct a comprehensive review that aims to systematically map, describe, and summarize the current utilization of artificial intelligence (AI) in the recruitment and retention of participants in clinical trials. MATERIALS AND METHODS: A comprehensive electronic search was conducted using the search strategy developed by the authors. The search encompassed research published in English, without any time limitations, which utilizes AI in the recruitment process of clinical trials. Data extraction was performed using a data charting table, which included publication details, study design, and specific outcomes/results. RESULTS: The search yielded 5731 articles, of which 51 were included. All the studies were designed specifically for optimizing recruitment in clinical trials and were published between 2004 and 2023. Oncology was the most covered clinical area. Applying AI to recruitment in clinical trials has demonstrated several positive outcomes, such as increasing efficiency, cost savings, improving recruitment, accuracy, patient satisfaction, and creating user-friendly interfaces. It also raises various technical and ethical issues, such as limited quantity and quality of sample size, privacy, data security, transparency, discrimination, and selection bias. DISCUSSION AND CONCLUSION: While AI holds promise for optimizing recruitment in clinical trials, its effectiveness requires further validation. Future research should focus on using valid and standardized outcome measures, methodologically improving the rigor of the research carried out.
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AI systems for clinical trial operations can parse biomarker requirements, prior therapies, and performance status from EHRs, identify potentially eligible patients, and flag candidates for screening.
“Systems parse biomarker requirements, prior therapies, and performance status from EHRs, identify potentially eligible patients, and flag candidates for screening [56,57,58,59,60,61,62].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [60]
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“73 Through evaluating hospital medical data and informing both physicians and patients about potential clinical trial opportunities, AI holds promise for expediting the process of finding suitable participants.”
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“75 The lack of adequate technical infrastructure to manage trial complexities without reliable adherence control and patient monitoring may result in underpowered trials, 10 while elevated nonadherence rates pose substantial challenges for sponsors and researchers.”
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“Benefits of AI tools Increasing efficiency, cost savings, and improving recruitment 1–6,8, Improving accuracy and predictive power 1,3–6,8–13,15,17,20,22,23,25,26,28,32–34, 36,42,44,46 Create user-friendly interfaces and improve patient satisfaction 43,45 Allow remote access and monitoring 13,40”
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“45”
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“51”
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“85 Additionally, NLP, a theory-driven range of computational techniques for automatic analysis and representation of human language from various unstructured data types, 86 has demonstrated its value in clinical trials.”
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“13 Sato et al 65 mentioned that AI tools could limit the applicability of the A4-fitted models to younger participants.”
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“Incomplete algorithm data training sets may lead to bias in patient inclusion selection, 37 and excessive trust tends to overlook the possibility of AI making mistakes.”
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“Unreliability of results under data constraints 1,2,5,8–11,13,15,16,18,19,21,23,27,28,31, 33,36,41,42,44 Limited generalizability 2,4–8,13–15, 17 , 19”
BackgroundVisual Field Endpoints for Neuroprotective Trials: A Case for AI-Driven Patient Enrichment
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“Hassan et al 25 and 3 other studies 13,18,53 showed that the use of AI in clinical trial recruitment should be transparent, with clear communication about how the technology works and the role it plays in the decision-making process.”
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“13 In light of these considerations, we have developed a comprehensive scoping review that aims to provide a detailed overview, description, and summary of the current status of AI implementation in recruitment and retention in clinical trials.”
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“Thirdly, Kehl et al 39 showed that the heterogeneity of the training and testing data significantly strengthens the model to identify patients.”
BackgroundRemote Health Monitoring in Clinical Trial using Machine Learning Techniques: A Conceptual Framework
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“Abiodun et al 76 developed a conceptual framework of remote health monitoring in a clinical trial using ML techniques, with various classifications to determine if a participant should be allowed to continue in the trial or not, but did not validate the effect of this framework.”
MethodJiang, Y., Guarino, P., Ma, S., Simon, S. D., Mayo, M. S., Raghavan, R., & Gajewski, B. J. (2016) · Trials · doi:10.1186/s13063-016-1457-3 · Cited by 16 · cited 1× · see citing works
“Benefits of AI tools Increasing efficiency, cost savings, and improving recruitment 1–6,8, Improving accuracy and predictive power 1,3–6,8–13,15,17,20,22,23,25,26,28,32–34, 36,42,44,46 Create user-friendly interfaces and improve patient satisfaction 43,45 Allow remote access and monitoring 13,40”
BackgroundPodichetty, J. T., Silvola, R. M., Rodriguez‐Romero, V., Bergstrom, R. F., Vakilynejad, M., Bies, R. R., & Stratford, R. E. (2021) · Clinical and Translational Science · doi:10.1111/cts.13035 · Cited by 16 · see citing works
Chen, B., Jin, H., Yang, Z., Qu, Y., Weng, H., & Hao, T. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0768-1 · Cited by 14 · see citing works
Prediction of clinical trial enrollment rates
Bieganek, C., Aliferis, C., & Ma, S. (2022) · PLoS ONE · doi:10.1371/journal.pone.0263193 · Cited by 14 · cited 2× · see citing works
“Unreliability of results under data constraints 1,2,5,8–11,13,15,16,18,19,21,23,27,28,31, 33,36,41,42,44 Limited generalizability 2,4–8,13–15, 17 , 19”
BackgroundMutasa, S., Chang, P., Sant, E. P. V., Nemer, J. S., Liu, M. Z., Karcich, J., Patel, G. S., Jambawalikar, S., & Ha, R. S. (2019) · Academic Radiology · doi:10.1016/j.acra.2019.08.012 · Cited by 12 · cited 2× · see citing works
“Firstly, Mutasa et al 30 showed that AI systems allow the computer to automatically construct predictive statistical models, tailored to solve a specific problem subset.”
BackgroundMethodAI-Radiomics Can Improve Inclusion Criteria and Clinical Trial Performance
Tomaszewski, M. R., Fan, S., García, A. L., Qi, J., Kim, Y., Gatenby, R. A., Schabath, M. B., Tap, W. D., Reinke, D. K., Makanji, R. J., et al. (2022) · Tomography · doi:10.3390/tomography8010028 · Cited by 12 · cited 1× · see citing works
“Tomaszewski et al 49 showed similar results, by identifying an enriched subset (52”
ResultBuilding a specialized lexicon for breast cancer clinical trial subject eligibility analysis
Jung, E., Jain, H. K., Sinha, A. P., & Gaudioso, C. (2021) · Health Informatics Journal · doi:10.1177/1460458221989392 · Cited by 11 · see citing works
Yaghy, A., Lee, A., Keane, P. A., Keenan, T. D. L., Mendonça, L. S. M., Lee, C. S., Cairns, A. M., Carroll, J., Chen, H., Clark, J., et al. (2022) · Experimental Eye Research · doi:10.1016/j.exer.2022.109092 · Cited by 11 · see citing works
Hassan, A. E., Ravi, S., Desai, S. K., Saei, H. M., Mckennon, E., & Tekle, W. G. (2023) · Interventional Neuroradiology · doi:10.1177/15910199231184604 · Cited by 11 · cited 3× · see citing works
“25 Informed consent was another ethical issue related to the use of AI tools.”
BackgroundMethodLeventakos, K., Helgeson, J. M., Mansfield, A. S., Deering, E., Schwecke, A., Adjei, A. A., Molina, J. R., Hocum, C. L., Parikh, K., Halfdanarson, T. R., et al. (2019) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2019.08.1240 · Cited by 8 · cited 1× · see citing works
“38 This system employs NLP to abstract patient and trial data from unstructured sources, aiding in matching patients to trials.”
MethodJordan, E., Jahreiss, L., & Kasi, P. M. (2021) · Journal of Gastrointestinal Oncology · doi:10.21037/jgo-21-175 · Cited by 7 · cited 3× · see citing works
“Unreliability of results under data constraints 1,2,5,8–11,13,15,16,18,19,21,23,27,28,31, 33,36,41,42,44 Limited generalizability 2,4–8,13–15, 17 , 19”
BackgroundMethodMaharjan, J., Ektefaie, Y., Ryan, L., Mataraso, S. J., Barnes, G., Shokouhi, S., Green‐Saxena, A., Calvert, J. S., Mao, Q., & Das, R. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2021.784250 · Cited by 7 · cited 2× · see citing works
“Unreliability of results under data constraints 1,2,5,8–11,13,15,16,18,19,21,23,27,28,31, 33,36,41,42,44 Limited generalizability 2,4–8,13–15, 17 , 19”
BackgroundCohort selection for clinical trials using multiple instance learning
Dai, H.-J., Wang, F.-D., Chen, C., Su, C.-H., Wu, C., & Jonnagaddala, J. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103438 · Cited by 6 · see citing works
A SuperLearner Approach to Predict Run-In Selection in Clinical Trials
Lanera, C., Berchialla, P., Lorenzoni, G., Acar, A. Ş., Chiminazzo, V., Azzolina, D., Gregori, D., & Baldi, I. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/4306413 · Cited by 4 · cited 1× · see citing works
“For example, Lanera et al 66 mentioned that the predictive performance of the proposed tool was not optimal for the case study considered due to the limited sample size.”
MethodEscudero, J., Zajicek, J., & Ifeachor, E. (2011) · Trials · doi:10.1186/1745-6215-12-s1-a18 · Cited by 1 · see citing works
Arcia, N., Dulawat, M. P. S., Sommerford, N., & Ghosh, B. (2020) · Value in Health · doi:10.1016/j.jval.2020.04.177 · Cited by 1 · see citing works
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- [7]Miao ZQ, Zhang QH, Qian YH, et al From human intelligence to its implementation model-granular computing theory and method. J Intell Syst. 2016; 11: 743- 757.
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Malheiro, V., Santos, B., Figueiras, A., & Mascarenhas‐Melo, F. (2025) · Pharmaceuticals · doi:10.3390/ph18060788 · Cited by 74 · see citing works
“This targeted approach not only accelerates recruitment but also improves population diversity and trial represen-tativeness [63].”
AI-driven innovations in pharmaceuticals: optimizing drug discovery and industry operations
Saini, J. P. S., Thakur, A., & Yadav, D. (2025) · RSC Pharmaceutics · doi:10.1039/d4pm00323c · Cited by 76 · see citing works
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Khan, S. N., Danishuddin, Khan, M. W. A., Guarnera, L., & Akhtar, S. M. F. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1743921 · Cited by 45 · see citing works
“AI also aids in clinical trials by accelerating patient recruitment, monitoring treatment responses, and identifying novel drug targets, thereby expediting the drug development process (Lu et al., 2024; Kaur and Butt, 2025).”
Vignesh, U., Parvathi, R., & Ram, K. G. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.103435 · Cited by 24 · see citing works
Health Equity Considerations in the Age of Artificial Intelligence
Budhu, J. A., Anderson, N., Branson, C. O., Dy-Hollins, M., Jiménez-Gómez, A., Nearing, K. I., Vélez, F. G. S., Staisch, J., Wabulya, A., & Mendizabal, A. (2025) · Neurology · doi:10.1212/wnl.0000000000214356 · Cited by 25 · see citing works
“…barriers; have higher risks of neurologic disease; or come from vulnerable populations; such as racial and ethnic minorities, sexual and gender minorities, 23 (SDOH) in the training of machine learning models can a ff ect their outputs, and the results of algorithms must be interpreted with caution.”
Use of AI as a Research Recruitment Tool: Ethical Elements, Considerations, and Recommendations
Rentzepis, P. J., Snyder, D. L., Baker, T. J., & Bierer, B. E. (2026) · The American Journal of Bioethics · doi:10.1080/15265161.2026.2649234 · Cited by 14 · see citing works
“…use of AI in clinical research and healthcare (Jobin et al. 2019; Cruz Rivera et al. 2020; Liu et al. 2020; Loi et al. 2020; Nagendran et al. 2020; Topol 2020; Khan et al. 2021; Grote 2022; Kargl et al. 2022; Bouhouita-Guermech et al. 2023; Collins et al. 2024; Lu et al. 2024; Youssef et al. 2024).”
Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment
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Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery
Kuno, M., Osumi, H., Udagawa, S., Yoshikawa, K., Ooki, A., Shinozaki, E., Ishikawa, T., Oba, J., Yamaguchi, K., & Sakurada, K. (2025) · Current Oncology · doi:10.3390/curroncol32110588 · Cited by 7 · see citing works
Artificial Intelligence in Nephrology: Pioneering Precision with Multimodal Intelligence
Jayaraman, P., Vasudev, I., Bhardwaj, A., Nadkarni, G. N., Sakhuja, A., & Meena, P. (2025) · Indian Journal of Nephrology · doi:10.25259/ijn_496_2024 · Cited by 7 · see citing works
Leveraging Large Language Models in Extracting Drug Safety Information from Prescription Drug Labels
Gisladottir, U., Zietz, M., Kivelson, S., Tanaka, Y., Sirdeshmukh, G., Brown, K. L., & Tatonetti, N. P. (2025) · Drug Safety · doi:10.1007/s40264-025-01594-x · Cited by 6 · see citing works
Seyhan, A. A., & Carini, C. (2026) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2026.105461 · Cited by 6 · see citing works
Singh, S., Kumar, S., Quadir, S. S., Bhandari, S., Baniya, B., Joshi, G., Jain, C. P., & Choudhary, D. K. (2025) · Journal of Pharmaceutical Innovation · doi:10.1007/s12247-025-09940-3 · Cited by 5 · see citing works
“Important evidence for AI's possible use in clinical trial design comes from its usage to enhance the data-driven strategy in clinical design and implementation [79] (Fig.”
The Role of Artificial Intelligence in Urogynecology: Current Applications and Future Prospects
de Oliveira, M. B. M., Mendes, F., Martins, M., Cardoso, P., Fonseca, J., Mascarenhas, T., & Mascarenhas, M. (2025) · Diagnostics · doi:10.3390/diagnostics15030274 · Cited by 5 · see citing works
“This way, AI introduction in research can help with trial design, implementation, and analysis, increase the identification and management of risks, and the research’s efficiency due to its automated nature and predictive power via ML and NLP [93].”
Gomase, V. S., Ghatule, A. P., Sharma, R., & Sardana, S. (2025) · Reviews on Recent Clinical Trials · doi:10.2174/0115748871366467250413070850 · Cited by 5 · see citing works
Machine Learning and Artificial Intelligence for Research on Hypertension
Khamissi, F. Z., Sun, L., Johnson, P., Shah, S., & Benjamin, I. J. (2025) · American Journal of Hypertension · doi:10.1093/ajh/hpaf051 · Cited by 4 · see citing works
Khalifa, A., & Hussein, R. (2026) · Journal of Medical Systems · doi:10.1007/s10916-026-02391-4 · Cited by 5 · see citing works
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“Such technology is already being used to improve recruitment in oncology clinical trials, e.g. by increasing efficiency, cost savings, improving recruitment, accuracy, patient satisfaction, and creating user-friendly interfaces [43].”
Leveraging blockchain and LLMs for patient–clinical trial matching
Hawashin, D., Salah, K., Jayaraman, R., Ellahham, S. H., & Yaqoob, I. (2025) · Array · doi:10.1016/j.array.2025.100657 · Cited by 4 · see citing works
AI/ML-based strategies for enhancing equity, diversity, and inclusion in randomized clinical trials
Abbidi, S. R., & Sinha, D. (2026) · Trials · doi:10.1186/s13063-026-09537-2 · Cited by 4 · see citing works
“Fairness-aware eligibility optimization can flag exclusion-ary criteria, while AI-based telehealth platforms improve participation of rural and mobility-limited patients [7].”
Yigamawano, F. K., Odom-Mabey, A. R., Xue, C., Pandey, H. K., & Kolachalama, V. B. (2025) · The Journal of Prevention of Alzheimer s Disease · doi:10.1016/j.tjpad.2025.100396 · Cited by 4 · see citing works
“…trial recruitment are available and have shown promise in enhancing efficiency, yet the primary hurdles remain their adoption and integration into clinical workflows, influenced by factors such as implementation barriers, lack of uniform standards, and the need for clinician literacy [4,23,24].”
Clinical Applications of Artificial Intelligence in Neurology Practice
Hadar, P., & Moura, L. M. V. R. (2025) · CONTINUUM Lifelong Learning in Neurology · doi:10.1212/cont.0000000000001552 · Cited by 4 · see citing works
The role of AI in oncology: present applications and future horizons
Weitzner, A. S., Singla, N., & Rai, A. (2026) · npj Precision Oncology · doi:10.1038/s41698-026-01408-y · Cited by 4 · see citing works
Yuan, T. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106628 · Cited by 3 · see citing works
Wójcik, S., Rulkiewicz, A., & Domienik‐Karłowicz, J. (2026) · Healthcare · doi:10.3390/healthcare14162519 · Cited by 3 · see citing works
“Broader reviews indicate that recruitment and retention are among the most frequently addressed operational targets for AI [51].”
The Adoption and Use of Artificial Intelligence and Machine Learning in Clinical Development
Lamberti, M. J., Florez, M. I., Do, H., Rosner, S., Ménard, T., Nielson, C. M., Donovan, A., Ye, J., Kaveripakam, S., Schoeberl, B., et al. (2025) · Therapeutic Innovation & Regulatory Science · doi:10.1007/s43441-025-00803-0 · Cited by 4 · see citing works
The role of artificial intelligence in pharmacy: Revolutionizing drug development and beyond
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Machine Learning in Clinical Trial Design and Optimization
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Artificial Intelligence and Cloud Computing in Healthcare
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Behavioral pattern analysis for adherence in clinical trials using sequence mining
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AI ‐Driven Epidemiology: The Next Frontier in Precision Public Health
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Liang, L., Lu, X., Jiao, D., Xiao, B., Fang, S. H. A. N., Zhang, R., Zhao, C., & Song, J. (2026) · BMC Psychiatry · doi:10.1186/s12888-026-08190-y · Cited by 2 · see citing works
“…and reasoning—to emulate, augment, and expand human intelligence across a broad spectrum of fields, including knowledge discovery, data mining, natural language processing (NLP) [19], computer vision, speech recognition, expert systems [20], conversational AI (CAI) [21], and generative AI [22].”
Kumarachari, R. K., Bukke, S. P. N., Kammara, L., Chandrakala, A. N., Himasaila, M., & Kamati, M. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-9730-5.ch004 · Cited by 1 · see citing works
Kachnowski, S., Khan, A. H., Floquet, S., Whitlock, K. K., Wisnivesky, J. P., Neill, D. B., Dankwa‐Mullan, I., Ortega, G., Daoud, M., Zaheer, R., et al. (2025) · Frontiers in Big Data · doi:10.3389/fdata.2025.1621526 · Cited by 1 · see citing works
A Meta-Analytic NLP-Driven Mapping of Fairness in Healthcare AI
Tsoni, R., Koutouzis, M., & Verykios, V. S. (2025) · International Conference on Information, Intelligence, Systems & Applications (IISA) · doi:10.1109/iisa66859.2025.11311222 · Cited by 1 · see citing works
“…for prediction [7], [8], medical imaging [9], [10], drug discovery [11]–[13], hiring processes in healthcare organizations [14], personalized patient treatment [15]–[17], medical simulation and training [18], [19], clinical trial optimization [20], [21], and many other relevant applications.”
Zhang, E., & Wang, D. P. (2025) · Scientific Reports · doi:10.1038/s41598-025-34208-8 · Cited by 2 · see citing works
Clinical applications of artificial intelligence in autosomal dominant polycystic kidney disease
Ebrahimi, N., Cheungpasitporn, W., Chebib, F. T., Borghol, A. H., Ghozloujeh, Z. G., Norouzi, S., & Abdipour, A. (2026) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfag010 · Cited by 1 · see citing works
AI-designed drugs: clinical development and future directions
Ahmed, R., Akhtar, S., Das, A., Begum, K., Sarma, A., Chakraborty, A., Neog, T., & Banu, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-34059-8.00010-5 · Cited by 1 · see citing works
Wang, Y., Miao, M., Wang, Q., Yin, Y., Zhao, H., Zhao, S., Yang, H., & WANG, X. (2026) · Drugs & Aging · doi:10.1007/s40266-026-01288-8 · Cited by 1 · see citing works
A review on artificial intelligence in pharmaceutical sciences: opportunities and challenges
Kumar, V., Kumar, U., Kumar, R., Tiwari, N., & Gupta, J. K. (2026) · Drug Development and Industrial Pharmacy · doi:10.1080/03639045.2026.2670435 · Cited by 1 · see citing works
“Ai-based Ai-based procedures are increasingly being used to improve recruitment and patient monitoring [185,186].”
Blockchain and Artificial Intelligence: Convergence, Applications and Challenges
Artenie, A. C., Morar, C. D., & Popescu, D. E. (2026) · International Conference on Development and Application Systems (DAS) · doi:10.1109/das69882.2026.11553350 · Cited by 1 · see citing works
“A broad review of artificial intelligence in clinical trials reported that AI substantially enhances the efficiency, precision, and overall satisfaction associated with participant recruitment [16].”
The role of artificial intelligence in clinical trials in adult and pediatric rheumatology
Bella, S. L., Rebollo-Giménez, A. I., Alongi, A., Capitoli, G., Breda, L., Torre, F. L., Venerito, V., & Ruperto, N. (2026) · Current Opinion in Immunology · doi:10.1016/j.coi.2026.102818 · Cited by 1 · see citing works
Artificial intelligence in drug development: From molecular discovery to precision medicine
Xie, T., Zhang, Z., Wang, Y., Wang, H., Chen, X., Feng, H., & Shao, F. (2026) · Genes & Diseases · doi:10.1016/j.gendis.2026.102402 · Cited by 1 · see citing works
Raj, A., Hasan, M., & Shahadat, S. B. (2026) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2026.101783 · Cited by 1 · see citing works
Pharma's AI Inflection Point: What Does It Mean for Early Phase Clinical Development?
Issa, A. M. (2025) · Clinical Pharmacology in Drug Development · doi:10.1002/cpdd.1596 · Cited by 1 · see citing works
“5,6 Accelerating recruitment has a real effect on cost and pipeline progression.”
From Design to Closure: Artificial Intelligence Transforming Clinical Research
Vats, K., & Alam, M. M. (2025) · Cureus · doi:10.7759/cureus.94895 · Cited by 1 · see citing works
Mirza, A. B., Fayez, F., & Ashkan, K. (2025) · Journal of Neuro-Oncology · doi:10.1007/s11060-025-05180-0
“AI-driven eligibility screening tools, integrated into electronic health records, can identify potentially suitable patients based on objective clinical, imaging, and demographic parameters [6].”
He, J., Zhao, M., Li, J., Schwebel, D. C., Fu, Y., Wang, W., Li, W., Huang, H., Zhao, S., Peng, R., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02134-8 · Cited by 1 · see citing works
AI in the discovery and development of antibody therapeutics
Peckner, R., Visweswaraiah, J., & Higginson-Scott, N. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-29808-0.00034-0
AI for clinical trials in oncology
Cerami, E., Riaz, I. B., & Kehl, K. L. (2025) · ESMO Real World Data and Digital Oncology · doi:10.1016/j.esmorw.2025.100658
The Rise of the Algorithmic Recruiter: How AI Explores the Ethical Boundaries of Talent Selection?
Eng, P. H., Liu, R. L., Yang, B., Ling, J., Liu, X. Y., & Kang, J. L. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-00250-1_40
Leveraging Generative AI in Clinical Studies to Improve Efficiency and Quality of Drug Development
Tang, W., Simone, L., Dwivedi, G., Xu, M., Zhu, X., & Chan, J. W. Y. (2026) · Springer eBooks · doi:10.1007/978-3-031-98022-0_25
Educating Future Leaders in Drug Development about AI
Graff, M., Mollet, A., & Szucs, T. D. (2026) · Education and human development · doi:10.5772/intechopen.1014237
Advancing dermatological clinical trials through artificial intelligence
Frasier, K., Hash, M. G., Remé, B., & Pham, D. (2026) · Archives of Dermatological Research · doi:10.1007/s00403-026-04530-w
“Furthermore, access disparities in AI infrastructure and data may widen global inequities, as most clinical trial applications are concentrated in high-income countries [18].”
InfluentialKakkar, R., & Byrne, M. F. (2026) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izag036
Artificial Intelligence in Recruitment: Frameworks and Innovations
Khurana, P., & Singla, J. (2026) · International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) · doi:10.1109/idciot67589.2026.11455650
“As an instance, there is a significant impact of dataset used to train a model on its accuracy [28].”
Artificial Intelligence (AI) and Machine Learning (ML) in Personalized Medicine
Paul, I. (2026) · Springer eBooks · doi:10.1007/978-981-95-6845-1_23
Totsika, V., & Perera, B. (2026) · Evidence-Based Nursing · doi:10.1136/ebnurs-2026-104580
Trends of Open-Source Tools and Frameworks for AI Drug Design
Mishra, S. K., Mukhia, P., Gurung, K., Rai, P., Roy, A., Chhetri, T., & Georrge, J. J. (2026) · Springer eBooks · doi:10.1007/978-3-032-20428-8_11
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Dhivya, P., Vanitha, C. N., Kavipriya, M., Nivedha, R., Priyanka, S., & Vijayasree, M. (2026) · International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) · doi:10.1109/icaiss68683.2026.11525703
“In [8], the scoping review is dedicated to AI-related recruitment in clinical trials, where the optimisation of candidate selection and retention is highlighted.”
Ethics of AI in Patient Recruitment and Diversity
Kholiya, N. S., Ojha, A., Singh, A. K., & Bhatt, V. (2026) · Journal of Drug Delivery and Therapeutics · doi:10.22270/jddt.v16i6.7815
Lewis, K. A., Seldomridge, A. N., Helmink, B. A., Lyu, H. G., & Snyder, R. A. (2026) · JCO Clinical Cancer Informatics · doi:10.1200/cci-26-00032
Meaningful Participation in AI-Mediated Clinical Trial Recruitment
Subrahmanyam, V. R., Bailar, M. A., Ostherr, K., Kostick, K. M., McGuire, A. L., Neitzke‐Spruill, L., & Rahimzadeh, V. N. (2026) · The American Journal of Bioethics · doi:10.1080/15265161.2026.2690962
“…clinical trial participation (One Study Team 2023), AI-mediated recruitment systems increasingly blur these distinctions by integrating recruitment, enrollment, retention, and participation histories within unified data structures designed for selecting ideal trial candidates (Lu et al. 2024).”
Owens, K., Kearns, L., & Bateman-House, A. (2026) · The American Journal of Bioethics · doi:10.1080/15265161.2026.2690940
“Artificial intelligence (AI) is often discussed as a means to improve the efficiency of clinical trial recruitment (Lu et al. 2024).”
Recruitment Inference Creep: Why AI in Clinical Trials Needs Auditable Ethical Limits
Ruíz-Vanoye, J. A., Díaz-Parra, O., Aguilar-Ortíz, J., Ruiz-Jaimes, M. Á., Toledo-Navarro, Y., Fuentes-Penna, A., Barrera-Cámara, R. A., & Trejo-Macotela, F. R. (2026) · The American Journal of Bioethics · doi:10.1080/15265161.2026.2690924
Industry Practitioners Developing or Implementing AI Platforms for Healthcare and Life Sciences
Atul Kumar Dnyaan Prasad Global University, Pulatov, M., Rejabbayevich, N. D., Almat, S., Рустамович, Х. Т., & Mardonovich, N. A. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-8904-2.ch017
Laurent, S., Dubois, P., Lefevre, M., & Moreau, C. (2026) · Journal of Artificial Intelligence for Healthcare Systems · doi:10.68159/t715039840
Brancone, S., Passante, C., & Prada, M. (2026) · Global & Regional Health Technology Assessment · doi:10.33393/grhta.2026.3813
Sukhadeve, A., Nair, N., & Bhise, T. (2026) · Journal of Clinical Sciences · doi:10.4103/jcls.jcls_110_25
Murray, B., Johnston, J. P., Dobbins, D. X., Doung, P., Fritz, M. K., Grizzle, M., Johnson, S. T., Leino, A. D., Macwan, R., Raheem, F., et al. (2026) · JACCP JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY · doi:10.1002/jac5.70300
“…intelligence also supports later-stage development through patient stratification, clinical trial design and execution (e.g., optimization of eligibility criteria, patient-trial matching, population enrichment, recruitment, adaptive trial design, remote monitoring), and drug repurposing [19, 39].”
InfluentialMajid, Z., Chaudhuri, W., & Kanabar, V. (2026) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-032-33123-6_16
AI virtual assistance in recruitment: a systematic literature review and bibliometric analysis
Stanikzai, M. E., & Mittal, E. (2026) · SAM Advanced Management Journal · doi:10.1108/samamj-06-2026-0038
- Authors
- Lu, X., Yang, C., Liang, L., Hu, G., Zhong, Z., & Jiang, Z.
- Affiliations
- University of Liverpool (GB)
- Department of Philosophy, School of Humanities, Central South University , Changsha, Hunan 410075,
- Central South University (CN)
- Queen Mary University of London (GB)
- Shenzhen Polytechnic University (CN)
- Corresponding author
- Jiang, Zihao
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-09-11
- Volume
- 31
- Issue
- 11
- Pages
- 2749-2759
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC11491624
- OpenAlex
- W4402499684
- Cited by
- 77
- References
- 82
- Citations by year
- 2026: 47 · 2025: 29 · 2024: 1
- FWCI
- 1.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of the American Medical Informatics Association (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Central South University Graduate Independent Innovation (2022ZZTS0333)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics in Clinical Research
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Keywords
- artificial intelligence, clinical trial recruitment, participant retention, clinical trials, cancer clinical trials, ethical issues of AI, data privacy, algorithmic discrimination, selection bias, patient satisfaction
- MeSH terms
- Artificial Intelligence, Clinical Trials as Topic, Humans, Patient Selection
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