Authors
Andy Ngo, Roland Tian, Amin Mohammad, Jenny L Weon, Simone Arvisais-Anhalt, Jacob Minor, Kenneth Youens
Published in
Journal of pathology informatics. Volume 23. Pages 100718. Epub Sep 12, 2026.
Abstract
Artificial intelligence (AI) in anatomic pathology is dominated by image-based diagnostic applications, but much of the specialty's cognitive work is textual: reviewing clinical history, drafting reports, coordinating testing, and communicating results. Large language models can be useful for many of these tasks, and agentic systems extend their capabilities by retrieving external information, invoking software tools, following task-specific instructions, tracking workflow states, and carrying out complex multi-step tasks under human oversight. This conceptual review outlines key components of agentic AI systems including retrieval augmented generation, tool use, skills, and the so-called "agentic loop"-and applies them to anatomic pathology workflows. Three illustrative design sketches use reflex testing support, workload balancing and case routing, and ancillary test decision support to explain how agentic systems can combine lab information system data, trustworthy knowledge sources, local policy, and explicit human approval gates. We examine considerations for clinical deployment, including interoperability and integration challenges, lifecycle governance and validation, security and safety concerns, and cost and sustainability. Agentic AI offers a plausible and constructive near-term vision for AI in pathology: better-supported diagnosis that allows pathologists to spend more of their time on the judgment, accountability, and genuine care that remain distinctly human.
PMID:
42894874
Bibliographic data and abstract were imported from PubMed on 19 Oct 2026.
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