The Analytical Framework of Clinical Trials Evaluating Clinical Outcomes of Artificial Intelligence-Based Digital Health Interventions: A Systematic Literature Review

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Abstract
Introduction: This systematic literature review (SLR) provides an analytical framework for clinical trials evaluating clinical outcomes of artificial intelligence-based digital health interventions (AI-DHI). Methods: The SLR was conducted in accordance with the PRISMA guidelines. Search was conducted (September 2025) in PubMed and Embase. Population included patients using AI-DHI. Only clinical trials exploring clinical outcomes, written in English, were considered. NICE checklist was used to assess studies’ quality. Results were analyzed descriptively. Results: Final sample had 84 studies, with metabolic (28.6%), musculoskeletal (20.2%), and mental health disorders (19.0%) as the most common indications. Most studies (75.0%) were controlled, parallel-group trials with 2+ arms, mostly comparing AI-DHI with standard-of-care or waitlist. Although type of intervention often precludes blinding (64.3% were open-label), a double-blinding is strongly recommended (only 6.0%). Only 9.5% of studies were conducted at multiple sites across different countries. Dropout rates in the total sample and each study arm should be <20% at all endpoints (64.3%). Statistical tests were used based on the outcome measures. The small sample sizes and limited generalizability of findings were reported as the main limitations. Conclusions: This SLR emphasized current methodological gaps and an urgent need for unified global guidelines. Standard SLR limitations apply to this research.

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