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Examines auditable AI variant classification in clinical settings, suggesting human oversight is crucial.
Agentic AI in care delivery is increasingly framed as autonomy with minimal oversight, and recent work places language models inside ACMG/AMP variant classification itself. We argue the opposite for auditable deployment: keep the decision core deterministic and free of model output, confining agents to an interpretation layer acting on already-classified results. Our central, falsifiable claim is that the load-bearing human gate sits upstream, at evidence admissibility, not at output release.
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Vladimir Mitev (2026) studied this question.
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