{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T07:02:36Z","timestamp":1793948556200,"version":"4.8.0"},"reference-count":22,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T00:00:00Z","timestamp":1782345600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosdigitalhealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Digit Health"],"abstract":"<jats:p>Metabolic dysfunction-associated steatotic liver disease (MASLD) is highly prevalent yet often underdiagnosed or undertreated in primary care due to asymptomatic early disease, uneven uptake of non\u2011invasive tests, limited elastography access, competing clinical priorities, and persistent challenges in sustaining lifestyle modification even after risk is recognized. This opinion introduces ATLAS\u2011Liver (Adaptive Triage and Learning Agent Suite for Liver disease) as a conceptual reference architecture, not a validated system, for how agentic artificial intelligence (AI) could support guideline\u2011aligned MASLD pathways by integrating risk estimation, explainability, calibration and fairness monitoring, curated guideline retrieval, and clinician\u2011retained decision authority within routine workflows. ATLAS\u2011Liver distinguishes between currently feasible components (e.g., probabilistic models using routine EHR data, local explanation layers with appropriate caveats, subgroup calibration checks, and version\u2011controlled guideline repositories) and aspirational elements such as dynamic retrieval\u2011augmented guidance, continuous drift surveillance, and automated agent\u2011level disagreement resolution. The framework is intended to complement established sequential pathways such as FIB\u20114 followed by elastography rather than replace them, offering potential value through improved workflow integration, transparency, follow\u2011through coordination, and equity monitoring. We situate ATLAS\u2011Liver within emerging work on AI agents in chronic liver disease while emphasizing its primary\u2011care orientation and governance\u2011focused design. We outline key implementation considerations as well as patient\u2011facing needs such as explanation formats, communication preferences, and support for lifestyle adherence. We acknowledge substantial limitations including lack of empirical validation. ATLAS\u2011Liver is offered as a hypothesis\u2011generating framework to guide responsible exploration of agentic AI in primary care MASLD pathways.<\/jats:p>","DOI":"10.1371\/journal.pdig.0001500","type":"journal-article","created":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T17:23:31Z","timestamp":1782408211000},"page":"e0001500","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":2,"title":["A conceptual agentic AI architecture for MASLD-associated significant fibrosis in primary care"],"prefix":"10.1371","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0714-4368","authenticated-orcid":true,"given":"Basile","family":"Njei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6791-1018","authenticated-orcid":true,"given":"Ulrick 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