aceAn agentic AI system enhances clinical detection of immune-related adverse events in a multi-phase validation study
Immune-related adverse events (irAEs) affect up to 40% of patients receiving immune checkpoint inhibitors, yet identification requires laborious, inconsistent manual chart review. Here we show that agentic artificial intelligence coupled with human verification could enhance efficiency, performance and consistency for irAE assessment. We developed and evaluated an agentic large language model system to extract the presence, temporality, severity grade, attribution and certainty of six irAE types from clinical no…
With AI assistance, clinical trial staff spent 2.71 fewer minutes per annotation, matched reference answers more often, and agreed with each other far more consistently when reviewing charts for immunotherapy side effects.
Severity grading was much weaker than event detection, and performance fell in prospective silent deployment compared with retrospective testing, so the system relies on human verification.
Evidence
- Peer-reviewedNature Cancer2026-10-29
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Truvace Impact Record TRV-2026-2318, v1: “An agentic AI system enhances clinical detection of immune-related adverse events in a multi-phase validation study.” Truvace, 2026-10-30. /record/TRV-2026-2318 (accessed at citation time). sha256 f249f4dca5334d01…
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