Implementation of Artificial Intelligence Approaches in Oncology Clinical Trials: A Systematic Review
Abstract
INTRODUCTION There is a growing interest in leveraging artificial intelligence (AI) technologies to enhance various aspects of clinical trials. The goal of this systematic review is to assess the impact of implementing AI approaches on different aspects of oncology clinical trials. METHODS Pertinent keywords were used to find relevant articles published in PubMed, Scopus, and Google Scholar databases, which described the clinical application of AI approaches. A quality evaluation utilizing a customized checklist specifically adapted was conducted. This study is registered with PROSPERO (CRD42024537153). RESULTS Out of the identified 2833 studies, 72 studies satisfied the inclusion criteria. Clinical Trial Enrollment & Eligibility were among the most commonly studied clinical trial aspects with 30 papers. The prediction of outcomes was covered in 25 studies of which 15 addressed the prediction of patients' survival and 10 addressed the prediction of drug outcomes. The trial design was studied in 10 articles. Three studies addressed each of the personalized treatments and decision-making, while one addressed data management. The results demonstrate using AI in cancer clinical trials has the potential to increase clinical trial enrollment, predict clinical outcomes, improve trial design, enhance personalized treatments, and increase concordance in decision-making. Additionally, automating some areas and tasks, clinical trials were made more efficient, and human error was minimized. Nevertheless, concerns and restrictions related to the application of AI in clinical studies are also noted. CONCLUSION AI tools have the potential to revolutionize the design, enrollment rate, and outcome prediction of oncology clinical trials.
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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 [61]
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Natural Language Processing for Patient Selection in Phase I or II Oncology Clinical Trials
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Interpretable machine learning for time-to-event prediction in medicine and healthcare
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Automated NLP Extraction of Clinical Rationale for Treatment Discontinuation in Breast Cancer
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Feasibility of institution‐agnostic, EHR‐integrated regional clinical trial matching
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AI-Radiomics Can Improve Inclusion Criteria and Clinical Trial Performance
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Building a specialized lexicon for breast cancer clinical trial subject eligibility analysis
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A Decision-Making Supporting Prediction Method for Breast Cancer Neoadjuvant Chemotherapy
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Automated contouring and statistical process control for plan quality in a breast clinical trial
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Increasing Clinical Trial Accrual via Automated Matching of Biomarker Criteria
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Predicting Clinical Trial Results: A Synthesis of Five Empirical Studies and Their Implications
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Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint
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Advancing cancer driver gene identification through an integrative network and pathway approach
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Triple and quadruple optimization for feature selection in cancer biomarker discovery
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Involvement in Chemotherapy Decision Making among Patients with Stage II and III Colon Cancer
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A method for matching patients to advanced prostate cancer clinical trials
Wagholikar, A. S., Nguyen, A. N., & Fung, M. (2014) · QUT ePrints (Queensland University of Technology) · Cited by 1 · see citing works
Flagged first, then by citations
4 of the 144 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
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
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The role of AI in oncology: present applications and future horizons
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“Nutrition in critical illness survivors: Why gaining a grip matters”
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A new microsedimentological conceptual framework of subglacial till formation
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Accelerating the clinical translation of bioengineered anticancer therapeutics
Zhao, G., Jiang, Y., Wang, C., Wang, S., & Li, N. (2026) · Journal of the National Cancer Center · doi:10.1016/j.jncc.2026.06.007 · Cited by 1 · see citing works
AI-based augmentation of oncology clinical trials
Villa, A., Eadie, A. L., Synnott, D., Romanò, R., Piffoux, M., Wong, E. Y. T., Scheinerman, N., Tan, D. S. W., de Braud, F., Koopman, M., et al. (2026) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-026-01189-0 · Cited by 3 · see citing works
Artificial Intelligence and Injectables in Cosmetic Dermatology
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The importance of ever-evolving phase 1 trials in oncology
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Implementation of Pharmacogenomics in Clinical Pharmacy Practice
Said, N. O. E., & Alshahawey, M. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-26472-6.00022-x
Future Horizons in Endocrine Oncology Therapeutics
Jamshidi, M., & Shalbaf, N. (2026) · doi:10.66224/infopub.2026.05.07
Shekhawat, M., Nagar, A. K., Gupta, A. A., Koshy, A. R., Naidu, V. S. S., Krishnamurthy, K. P., & Sathyakkagari, N. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106739
- Authors
- Saady, M., Eissa, M., Yacoub, A. S., Hamed, A. B., & Azzazy, H. M. E.-S.
- Affiliations
- American University in Cairo (EG)
- Salisbury District Hospital (GB)
- The University of Texas at Arlington (US)
- Future University in Egypt (EG)
- Corresponding author
- Azzazy, Hassan Mohamed El-Said
- Venue
- Artificial Intelligence in Medicine
- Publisher
- Elsevier BV
- Published
- 2025-03
- Volume
- 161
- Pages
- 103066
- ISSN
- 0933-3657 (ISSN-L)
- OpenAlex
- W4406549900
- Semantic Scholar
- 55afe0f91d193e25a3cde8b1d4e93bdaf75910f9
- Cited by
- 15
- References
- 144
- Citations by year
- 2026: 7 · 2025: 8
- FWCI
- 0.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Artificial Intelligence in Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- The American University in Cairo
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence, cancer clinical trials, clinical trial recruitment, eligibility determination, outcome prediction, survival prediction, drug response prediction, trial design, personalized treatment, clinical decision-making, clinical data management
- MeSH terms
- Artificial Intelligence, Clinical Trials as Topic, Humans, Medical Oncology, Neoplasms
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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