This white paper examines how machine learning (ML) can improve efficiency, consistency, and scalability in clinical trial medical review. Traditional medical review processes rely heavily on manual workflows, fragmented data, and retrospective analysis, leading to delays and increased risk. The paper explains how ML-powered analytics enable earlier signal detection, automated data triage, and prioritization of high-risk subjects or events. By integrating clinical, safety, and operational data into centralized analytics platforms, organizations can reduce manual effort while maintaining regulatory compliance. Use cases include adverse event monitoring, protocol deviation detection, and medical review optimization. The paper positions ML as a decision-support tool—not a replacement for medi

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