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Verification Debt as Agentic Artificial Intelligence Accelerates Clinical Analysis
ABSTRACT
Clinical prediction research is becoming easier to produce than to verify. Agentic artificial intelligence (AI) may widen this gap by reducing the cost of producing claims more than the cost of verifying them. We hypothesize that unresolved verification work will therefore accumulate around questions that require external evidence or expert judgment. We call each such question a check and define verification debt as the set of unresolved checks required to support claims already in use. This Viewpoint argues that verification capacity, rather than analytical speed, will determine which AI-assisted clinical claims can be relied on. We propose claim-level records that make unresolved checks visible, assign responsibility, link checks to dependent claims, and document three closure decisions: supported, revised, or withdrawn. We outline roles for journals, research platforms, tool developers, and funders. We propose two criteria for evaluating the framework: consequential errors prevented or corrected, and expert time required.
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Copyright
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