Use of artificial intelligence for cancer clinical trial enrollment: a systematic review and meta-analysis
Open accessAbstract
Background: The aim of this study is to provide a comprehensive understanding of the current landscape of artificial intelligence (AI) for cancer clinical trial enrollment and its predictive accuracy in identifying eligible patients for inclusion in such trials. Methods: Databases of PubMed, Embase, and Cochrane CENTRAL were searched until June 2022. Articles were included if they reported on AI actively being used in the clinical trial enrollment process. Narrative synthesis was conducted among all extracted data: accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. For studies where the 2x2 contingency table could be calculated or supplied by authors, a meta-analysis to calculate summary statistics was conducted using the hierarchical summary receiver operating characteristics curve model. Results: Ten articles reporting on more than 50 000 patients in 19 datasets were included. Accuracy, sensitivity, and specificity exceeded 80% in all but 1 dataset. Positive predictive value exceeded 80% in 5 of 17 datasets. Negative predictive value exceeded 80% in all datasets. Summary sensitivity was 90.5% (95% confidence interval [CI] = 70.9% to 97.4%); summary specificity was 99.3% (95% CI = 81.8% to 99.9%). Conclusions: AI demonstrated comparable, if not superior, performance to manual screening for patient enrollment into cancer clinical trials. As well, AI is highly efficient, requiring less time and human resources to screen patients. AI should be further investigated and implemented for patient recruitment into cancer clinical trials. Future research should validate the use of AI for clinical trials enrollment in less resource-rich regions and ensure broad inclusion for generalizability to all sexes, ages, and ethnicities.
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AI is employed in clinical trials to accelerate enrollment, monitor patients (e.g., wearables), and improve retention.
“Already, AI is employed in clinical trials to accelerate enrollment, monitor patients (e.g., wearables), and improve retention [29,114,115].”
From Bias in medical AI: Implications for clinical decision-making (2024) · cites this paper as [115]
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 [58]
A 2022 meta-analysis of 10 published studies across commercial and academic platforms found that most clinical trial matching applications offered comparable or superior performance to manual screening, with dramatic efficiency improvements.
“A 2022 meta-analysis reported on 10 published studies across multiple commercial and academic platforms and found that most applications offered comparable, if not superior performance to manual screening, with dramatic improvements in efficiency (149).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [149]
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- Authors
- Chow, R., Midroni, J., Kaur, J., Boldt, G., Liu, G., Eng, L., Liu, F.-F., Haibe-Kains, B., Lock, M., & Raman, S.
- Affiliations
- Western University (CA)
- University Health Network (CA)
- Princess Margaret Cancer Centre, University Health Network, Temerty Faculty of Medicine, University of Toronto , Toronto, ON, Canada
- Corresponding author
- Raman, Srinivas
- ORCID
- Venue
- JNCI: Journal of the National Cancer Institute
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-01-23
- Volume
- 115
- Issue
- 4
- Pages
- 365-374
- ISSN
- 0027-8874 (ISSN-L)
- 1460-2105
- PMC
- PMC10086633
- OpenAlex
- W4317781007
- Cited by
- 65
- References
- 27
- Citations by year
- 2026: 27 · 2025: 22 · 2024: 13 · 2023: 3
- FWCI
- 0.76
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JNCI Journal of the National Cancer Institute (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- CARO-CROF Pamela Catton Summer Studentship Award
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Keywords
- artificial intelligence, cancer clinical trials, clinical trial recruitment, prediction accuracy, sensitivity and specificity, patient recruitment
- MeSH terms
- Artificial Intelligence, Humans, Neoplasms, Predictive Value of Tests, ROC Curve, Sensitivity and Specificity
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