{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T17:34:27Z","timestamp":1778780067693,"version":"3.51.4"},"reference-count":88,"publisher":"Oxford University Press (OUP)","issue":"11","license":[{"start":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T00:00:00Z","timestamp":1726012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Central South University Graduate Independent Innovation","award":["2022ZZTS0333"],"award-info":[{"award-number":["2022ZZTS0333"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Discussion and Conclusion<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocae243","type":"journal-article","created":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T02:53:04Z","timestamp":1726109584000},"page":"2749-2759","source":"Crossref","is-referenced-by-count":58,"title":["Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review"],"prefix":"10.1093","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5835-1192","authenticated-orcid":false,"given":"Xiaoran","family":"Lu","sequence":"first","affiliation":[{"name":"Department of Philosophy, School of the Art, University of Liverpool , Liverpool L69 3BX,","place":["United Kingdom"]}]},{"given":"Chen","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Philosophy, School of Humanities, Central South University , Changsha, Hunan 410075,","place":["P.R. China"]}]},{"given":"Lu","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Philosophy, School of Humanities, Central South University , Changsha, Hunan 410075,","place":["P.R. China"]}]},{"given":"Guanyu","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi\u2019an Jiaotong University , Xi\u2019an, Shanxi 710049,","place":["P.R. China"]},{"name":"School of Electronic Engineering and Computer Science, Queen Mary University of London , London E1 4NS,","place":["United Kingdom"]}]},{"given":"Ziyi","family":"Zhong","sequence":"additional","affiliation":[{"name":"Institute of Life Course and Medical Sciences, University of Liverpool , Liverpool L69 3BX,","place":["United Kingdom"]}]},{"given":"Zihao","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Marxism, Shenzhen Polytechnic University , Shenzhen, Guangdong 518055,","place":["P.R. 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