Trial Factors Associated With Completion of Clinical Trials Evaluating AI: Retrospective Case-Control Study

• Published in: Journal of medical Internet research Vol. 26; no. 1; p. e58578
• Main Authors: Chen, David, Cao, Christian, Kloosterman, Robert, Parsa, Rod, Raman, Srinivas
• Format: Journal Article
• Language: English
• Published: Canada Journal of Medical Internet Research 23.09.2024; JMIR Publications
• Subjects: Analysis; Artificial Intelligence; Case-Control Studies; Clinical trials; Clinical Trials as Topic - statistics & numerical data; Experimental design; Female; Health aspects; Humans; Male; Medical laboratory technology; Medical technology; Original Paper; Quality management; Research Design; Retrospective Studies; Testing; United States; clinical trial; completion; Europe; AI; health information; informatics; artificial intelligence; clinical; application; cross-sectional study; intervention; logistic regression; trial design; health care; trials testing
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/58578

Evaluation of artificial intelligence (AI) tools in clinical trials remains the gold standard for translation into clinical settings. However, design factors associated with successful trial completion and the common reasons for trial failure are unknown. This study aims to compare trial design factors of complete and incomplete clinical trials testing AI tools. We conducted a case-control study of complete (n=485) and incomplete (n=51) clinical trials that evaluated AI as an intervention of ClinicalTrials.gov. Trial design factors, including area of clinical application, intended use population, and intended role of AI, were extracted. Trials that did not evaluate AI as an intervention and active trials were excluded. The assessed trial design factors related to AI interventions included the domain of clinical application related to organ systems; intended use population for patients or health care providers; and the role of AI for different applications in patient-facing clinical workflows, such as diagnosis, screening, and treatment. In addition, we also assessed general trial design factors including study type, allocation, intervention model, masking, age, sex, funder, continent, length of time, sample size, number of enrollment sites, and study start year. The main outcome was the completion of the clinical trial. Odds ratio (OR) and 95% CI values were calculated for all trial design factors using propensity-matched, multivariable logistic regression. We queried ClinicalTrials.gov on December 23, 2023, using AI keywords to identify complete and incomplete trials testing AI technologies as a primary intervention, yielding 485 complete and 51 incomplete trials for inclusion in this study. Our nested propensity-matched, case-control results suggest that trials conducted in Europe were significantly associated with trial completion when compared with North American trials (OR 2.85, 95% CI 1.14-7.10; P=.03), and the trial sample size was positively associated with trial completion (OR 1.00, 95% CI 1.00-1.00; P=.02). Our case-control study is one of the first to identify trial design factors associated with completion of AI trials and catalog study-reported reasons for AI trial failure. We observed that trial design factors positively associated with trial completion include trials conducted in Europe and sample size. Given the promising clinical use of AI tools in health care, our results suggest that future translational research should prioritize addressing the design factors of AI clinical trials associated with trial incompletion and common reasons for study failure.