{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T16:54:32Z","timestamp":1792169672783,"version":"4.4.0"},"reference-count":20,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T00:00:00Z","timestamp":1792108800000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Interpretive clinical artificial intelligence (AI), in which a clinician interprets an image or signal and an AI system offers a competing or confirming read, is increasingly routine in radiology, pathology, dermatology, and endoscopy. Oversight of these systems assumes that independent human judgment occurred before AI influence entered the decision. The informatics infrastructure, meaning the electronic health record, its audit logs, and the reporting systems built on them, does not verify this.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>We name the missing assurance layer decision integrity, situate it between cybersecurity verification and clinical quality review, and argue it is a measurement construct distinct from diagnostic accuracy. Drawing on the logic of preregistration, we propose a candidate architecture, a minimal six-event record under sequential disclosure, preserving tamper-evident evidence of pre-AI judgment, post-AI revision, and rationale.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The architecture is a candidate for evaluation, not a validated standard; its purpose is to make clinician independence auditable rather than assumed.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocag152","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T11:13:23Z","timestamp":1786706003000},"source":"Crossref","is-referenced-by-count":0,"title":["The missing layer: decision integrity as a verifiable assurance layer in interpretive clinical artificial intelligence"],"prefix":"10.1093","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1443-8019","authenticated-orcid":false,"given":"James","family":"Thornton","sequence":"first","affiliation":[{"name":"Cyber-Physical Engineering , Hot Springs, AR 71901,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7864-287X","authenticated-orcid":false,"given":"Joshua M","family":"Henderson","sequence":"additional","affiliation":[{"name":"Graduate School of Education, Fordham University , New York, NY 10023,","place":["United States"]},{"name":"Evidify LLC , East Orange, NJ 07017,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,10,16]]},"reference":[{"key":"2026101612373123100_ocag152-B1","first-page":"15","article-title":"Natural language processing improves coding accuracy","volume":"11","author":"Payne","year":"2011","journal-title":"MGMA Connex"},{"key":"2026101612373123100_ocag152-B2","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1136\/amiajnl-2011-000089","article-title":"Automation bias: a systematic review of frequency, effect mediators, and mitigators","volume":"19","author":"Goddard","year":"2012","journal-title":"J Am Med Inform Assoc"},{"key":"2026101612373123100_ocag152-B3","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1093\/jamia\/ocw105","article-title":"Automation bias and verification complexity: a systematic review","volume":"24","author":"Lyell","year":"2017","journal-title":"J Am Med Inform Assoc"},{"key":"2026101612373123100_ocag152-B4","doi-asserted-by":"crossref","first-page":"e222176","DOI":"10.1148\/radiol.222176","article-title":"Automation bias in mammography: the impact of artificial intelligence BI-RADS suggestions on reader performance","volume":"307","author":"Dratsch","year":"2023","journal-title":"Radiology"},{"key":"2026101612373123100_ocag152-B5","doi-asserted-by":"crossref","first-page":"2275","DOI":"10.1001\/jama.2023.22295","article-title":"Measuring the impact of AI in the diagnosis of hospitalized patients","volume":"330","author":"Jabbour","year":"2023","journal-title":"JAMA"},{"key":"2026101612373123100_ocag152-B6","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1177\/0018720810376055","article-title":"Complacency and bias in human use of automation: an attentional integration","volume":"52","author":"Parasuraman","year":"2010","journal-title":"Hum Factors"},{"key":"2026101612373123100_ocag152-B7","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1016\/S2468-1253(25)00133-5","article-title":"Endoscopist deskilling risk after exposure to artificial intelligence in colonoscopy","volume":"10","author":"Budzyn","year":"2025","journal-title":"Lancet Gastroenterol Hepatol"},{"key":"2026101612373123100_ocag152-B8","doi-asserted-by":"crossref","first-page":"1506","DOI":"10.1177\/0018720814535628","article-title":"The retention of manual flying skills in the automated cockpit","volume":"56","author":"Casner","year":"2014","journal-title":"Hum Factors"},{"key":"2026101612373123100_ocag152-B9","volume-title":"Top 10 Patient Safety Concerns 2026","author":"ECRI","year":"2026"},{"key":"2026101612373123100_ocag152-B10","volume-title":"Review of VHA\u2019s Use of Generative Artificial Intelligence. 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