AI-powered clinical trial eligibility and safety using Amazon Bedrock AgentCore

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AI agents built on Amazon Bedrock AgentCore let clinical trial teams make fast, accurate enrollment decisions while keeping clinicians in control through human-in-the-loop oversight. According to the Tufts Center for the Study of Drug Development, 80 percent of clinical trials miss their enrollment timelines, and each day of delay costs an estimated $500,000. Today, eligibility decisions rely on manual chart review across fragmented sources — EHR notes, lab results, imaging reports, and medication histories. Study teams spend hours reconstructing each candidate’s history and mapping it to protocol criteria. As protocols grow more complex, this doesn’t scale: screen failure rates stay high and enrollment targets slip. We show how to architect a Clinical Trial Eligibility and Safety Agent on AWS that assembles patient evidence, evaluates it against protocol criteria, and presents screening recommendations with citations, while clinicians retain final authority and full audit trails. It combines AWS HealthLake for FHIR-native data access, Amazon Bedrock AgentCore for multi-step reasoning, and Amazon Bedrock AgentCore Evaluations for scoring each decision via LLM-as-a-judge and human-in-the-loop. This post is for solution architects, engineering teams, and technology leaders applying AI to clinical trial operations on AWS. AI agents for clinical trial screening AI agents with Human-in-the-Loop (HIL) are well-suited for clinical trial eligibility and safety decisions because they address information fragmentation while preserving human clinical judgment. The core problem …

Vollständiger Originalartikel ↗

• Betroffenheit im eigenen Stack prüfen: Versionen/Komponenten abgleichen.
• Originalquelle / Hersteller-Advisory lesen: https://aws.amazon.com/blogs/architecture/ai-agents-for-clinical-trial-screening/
• Verfügbaren Patch oder Workaround einspielen und dokumentieren.