A Quantitative Analysis of Global Artificial Intelligence-related Medical Studies: An Urgent Call for More, and Better Randomized Controlled Trials

This study-level dataset underpins the scoping review “A Quantitative Analysis of Global Artificial Intelligence-related Medical Studies: An Urgent Call for More, and Better Randomized Controlled Trials.” It includes all 4,667 primary studies with a substantial AI component that were extracted from 218 systematic reviews of medical AI published between 2012 and 2024. Each row corresponds to one primary study and contains standardized variables describing: bibliographic information (review ID, first author, publication year), research stage along the AI development pathway (preclinical development/offline validation, early live clinical evaluation, comparative prospective evaluation/RCT), medical specialty (mapped to first-level ICD-11 categories), country/region, center type (single center, single-country multicenter, multinational multicenter), sample size, and the AI role in clinical practice (diagnostic yield/performance, clinical decision-making, care management, patient behavior/symptoms). For studies identified as randomized controlled trials, additional fields capture design characteristics (e.g., comparator type, randomization unit), primary outcome type and direction of effect, and self-reported adherence to reporting guidelines. The dataset contains no patient-level data and only includes information available from published articles. It is intended to facilitate secondary analyses of temporal trends, geographic and specialty concentration, study design features, and reporting practices in medical AI research, and to support methodological, policy, and implementation work aimed at improving the evidence base for AI in healthcare.