The Clinical Trial Pipeline Reveals the Next Wave of Artificial Intelligence in Healthcare: A Multidimensional Analysis of 8,532 Registered Studies

Abstract

The prospective clinical evaluation of artificial intelligence in medicine has expanded rapidly, but the global AI clinical trial landscape remains incompletely characterized. We systematically identified AI-related trials registered in ClinicalTrials.gov using a broad keyword search followed by an LLM-based classifier. Each trial was classified across seven dimensions: clinical function, data modality, specialty, AI integration and autonomy, workflow position, translational maturity, and epistemic role. We identified 8,532 AI clinical trials across 32 specialties, with 80% registered from 2019 onward and 30.5% using a randomized controlled design. Imaging-based AI was the largest modality, with 2,475 trials (29%), while clinical text and NLP trials increased seven-fold between 2018 and 2025. Prognostic AI (4,324 trials) slightly exceeded diagnostic AI (3,828 trials), suggesting a shift from disease detection toward risk stratification and trajectory prediction. Treatment recommendation remained less developed, with 768 trials (9%). Translational maturity remained limited: 3,259 trials (38%) were retrospective validation studies and 1,802 (21%) were silent prospective evaluations, indicating that much of the pipeline still produces algorithmic rather than clinical evidence. Only 184 trials involved Level 4 semi-autonomous or closed-loop AI, 68% of which focused on glucose management. Multimodal AI accounted for 33.6% of trials, mainly combining imaging, omics, physiological signals, and wearable data. These findings indicate that clinical AI has moved from retrospective development to prospective evaluation, but not yet to adequately powered, geographically representative, long-term outcome trials. Future progress will depend on closing gaps in trial scale, specialty coverage, geographic representation, and translational maturity.