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AI for Clinical Trials
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== <span style="color: #FFFFFF;">Creating</span> == Designing an AI-enhanced clinical trial: (1) Site selection: ML on prior trial data to identify high-enrolling, high-quality sites. (2) Protocol optimization: NLP analysis of failed trials in same indication; avoid common failure modes. (3) Eligibility matching: embed AI patient finder in target hospital EHR systems. (4) Dropout risk: enroll dropout risk model at randomization; trigger retention protocols for high-risk patients at 30-day mark. (5) Adaptive design: pre-specify all adaptation rules in protocol; simulate 10,000 trial scenarios; submit adaptive design strategy to FDA for feedback before starting. (6) Real-time monitoring: automated safety and efficacy dashboards for DSMB; ML flags unusual patterns. [[Category:Artificial Intelligence]] [[Category:Clinical Trials]] [[Category:Healthcare]] </div>
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