Artificial Intelligence and Mobile Technologies in Clinical Trial Operations and Recruitment in Cancer Research: A Four-Year Retrospective Analysis of Adult Oncology Trials
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Abstract
Patient recruitment and retention remain persistent challenges in oncology clinical trials, often leading to delays, increased costs and compromised data validity. Traditional recruitment strategies frequently fail to identify suitable participants efficiently which may result in under-enrollment and high dropout rates that limit the generalizability of trial outcomes. This paper examines how the use of artificial intelligence (AI) and mobile technologies affect patient recruitment and retention in oncology clinical trials. Employing a retrospective analysis of adult oncology trials conducted in the United States between 2017 and 2021, the study utilizes data from adult clinical trial registries to evaluate recruitment patterns and outcomes. Findings demonstrate that AI and mobile health (mHealth) tools can significantly streamline recruitment processes, reduce participant attrition and enhance overall trial efficiency. Integrating these technologies into clinical research operations may aid oncology trials to achieve more reliable data, accelerate therapeutic discoveries and ultimately contribute to improved cancer care outcomes. The study concludes that the integration of AI and mobile health technologies will not only improve operational efficiency but also help address disparities in participation, particularly among underrepresented groups. Therefore, this study recommends that clinical trial administrators should adopt AI and mobile health (mHealth) tools for patient matching, recruitment optimization and retention tracking.
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Copyright (c) 2025 Ome Valentina Akpughe, Timothy Olorundare
This work is licensed under a Creative Commons Attribution 4.0 International License.
Creative Commons License All articles published in Annals of Medicine and Medical Sciences are licensed under a Creative Commons Attribution 4.0 International License.
Ome Valentina Akpughe, Health Informatics, Harrisburg University of Science and Technology, Harrisburg, USA.
Health Informatics, Harrisburg University of Science and Technology, Harrisburg, USA.
Timothy Olorundare, Health Informatics, Harrisburg University of Science and Technology, Harrisburg, USA.
Health Informatics, Harrisburg University of Science and Technology, Harrisburg, USA.
[1] Chen, J., Lu, Y., & Kummar, S. (2022). Increasing patient participation in oncology clinical trials. Cancer Medicine, 12(3), 2219–2226. https://doi.org/10.1002/cam4.5150
[2] Chow, R., Midroni, J., Kaur, J., Boldt, G., Liu, G., Eng, L., Liu, F., Haibe-Kains, B., Lock, M., & Raman, S. (2023). Use of artificial intelligence for cancer clinical trial enrollment: a systematic review and meta-analysis. JNCI Journal of the National Cancer Institute, 115(4), 365–374. https://doi.org/10.1093/jnci/djad013
[3] Idossa, D., Patel, S., & Florez, N. (2023). Enhancing patient retention in Clinical Trials-Strategies for Success. JAMA Oncology, 9(8), 1031. https://doi.org/10.1001/jamaoncol.2023.1341
[4] Ismail, A., Al-Zoubi, T., Naqa, I. E., & Saeed, H. (2023). The role of artificial intelligence in hastening time to recruitment in clinical trials. BJR|Open, 5(1). https://doi.org/10.1259/bjro.20220023
[5] Marino, M., Kazmaier, L., Krendelsberger, A., Müller, S., Kesting, S., Fey, T., & Nasseh, D. (2024). How can current oncological datasets be adjusted to support the automated patient recruitment in clinical trials? Health Informatics Journal, 30(1). https://doi.org/10.1177/14604582241235632
[6] Moffat, K. R., Shi, W., Cannon, P., & Sullivan, F. (2023). Factors associated with recruitment to randomised controlled trials in general practice: a systematic mixed studies review. Trials, 24(1). https://doi.org/10.1186/s13063-022-06865-x
[7] Mudaranthakam, D. P., Alsup, A. M., Murakonda, V., Lin, T., Thompson, J., Gajewski, B., & Mayo, M. S. (2022). Accelerating cancer patient recruitment through a mobile application (Clinical Trial Finder). Cancer Informatics, 21, 117693512110731. https://doi.org/10.1177/11769351211073114
[8] Mudaranthakam, D. P., Thompson, J., Hu, J., Pei, D., Chintala, S. R., Park, M., Fridley, B. L., Gajewski, B., Koestler, D. C., & Mayo, M. S. (2018). A Curated Cancer Clinical Outcomes Database (C3OD) for accelerating patient recruitment in cancer clinical trials. JAMIA Open, 1(2), 166–171. https://doi.org/10.1093/jamiaopen/ooy023
[9] Reynolds, S. A., O’Connor, L., McGee, A., Kilcoyne, A. Q., Connolly, A., Mockler, D., Guinan, E., & O’Neill, L. (2023). Recruitment rates and strategies in exercise trials in cancer survivorship: a systematic review. Journal of Cancer Survivorship. https://doi.org/10.1007/s11764-023-01363-8
[10] Verdini, N. P., Bryl, K. L., Baser, R. E., Lapen, K., Mao, J. J., & Gillespie, E. F. (2024). Patient-Reported Outcomes as a recruitment strategy for clinical trial enrollment. JAMA Oncology, 10(6), 784. https://doi.org/10.1001/jamaoncol.2024.0280