Clinical trials are expensive ($2.6B average drug development cost) and AI can significantly reduce costs and timelines. Patient-trial matching: NLP extracts eligibility criteria from trial protocols, matches against patient EHR data. Reduces screening time from weeks to hours. Reduces screen failure rates 30-50%. Protocol design: AI analyzes historical trial data to recommend optimal dosing regimens, endpoint selection, visit schedules. Bayesian adaptive trial designs use interim results to adjust allocation. Dropout prediction: LSTM models on patient visit history, survey responses, and biomarkers predict dropout risk 3-6 weeks ahead. Proactive retention interventions reduce dropout 20-30%. Adverse event detection: NLP on patient diaries and clinical notes for early adverse event signals. Anomaly detection in lab values identifies safety signals faster than traditional methods. Site selection: ML models predict site performance (enrollment rate, data quality) from historical metrics, enabling optimal site selection. Endpoint analysis: ML for biomarker-based patient stratification finds subpopulations most likely to respond. Improves statistical power, reduces trial size. Regulatory: FDA uses predictive analytics in drug review. AI-assisted regulatory submissions emerging. Critical caveat: clinical trial AI must be validated, interpretable, and operate within stringent regulatory frameworks. FDA expects transparent, documented AI decision support.