{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T17:46:51Z","timestamp":1791740811278,"version":"4.3.4"},"reference-count":222,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T00:00:00Z","timestamp":1791676800000},"content-version":"vor","delay-in-days":10,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Medicine"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Healthcare services and data ecosystems remain fragmented, inequitable and misaligned with patient, clinician and public health needs. Three pathways to patient\u2010centred Artificial Intelligence (AI) have emerged to address this: data collaboration, Whole System Approaches and digital inclusion. Each has advanced the field. However, none delivers the continuous, participatory and outcome\u2010grounded learning that equitable AI demands. The populations whose data drives AI learning must also be the populations who benefit from it. This principle has not been structurally embedded in any existing pathway. This narrative review defines a framework for operationalising Adaptive Machine Learning (AML) as a fourth, integrative pathway to patient\u2010centred AI. AML is defined as continuous learning from real\u2010world individual\u2010level data to generate personalised, population\u2010level insights returning direct benefit to the communities who produce them. A narrative review of PubMed and Google Scholar (2018\u20132025) was conducted across five thematic domains: AI and machine learning in healthcare; real\u2010world data and evidence; patient\u2010centred care and digital health; data governance and AI regulation; and adaptive clinical trial design. Search terms covered healthcare AI applications and adaptive, continuously learning systems, emphasising prediction\u2010oriented rather than static diagnostic approaches. Eligibility was determined through iterative thematic analysis. Eight systemic barriers to patient\u2010centred AI are identified and the three existing pathways critically examined for limitations in addressing bias, validation and equitable implementation. AML is proposed as the mechanism that closes this gap, connecting systems medicine, personalised medicine and precision medicine through continuous, outcome\u2010based learning, identifying three original conceptual contributions as enablers: Integrative Data Governance (IDG), Adaptive Clinical Trial (ACT) designs and Evidence Sandbox Facilities (ESFs). Progress requires globally standardised Healthcare Data Recycling frameworks, adaptive evaluation methodologies and ESFs enabling multi\u2010stakeholder validation. Future research should address federated AML validation, collective consent mechanisms and alignment of regulatory frameworks with international AI governance standards.<\/jats:p>","DOI":"10.1002\/cam4.72371","type":"journal-article","created":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T17:37:09Z","timestamp":1791740229000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Advancing Patient\u2010Centred\n                    <scp>AI<\/scp>\n                    With Adaptive Machine Learning"],"prefix":"10.1002","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2765-8326","authenticated-orcid":false,"given":"Dimitrios","family":"Kalogeropoulos","sequence":"first","affiliation":[{"name":"Global Health &amp; Digital Innovation Foundation  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7906-698X","authenticated-orcid":false,"given":"Paul","family":"Barach","sequence":"additional","affiliation":[{"name":"School of Medicine Thomas Jefferson University  Philadelphia Pennsylvania USA"},{"name":"Sheps Health Services Research Center University of North Carolina  Chapel Hill North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8401-8686","authenticated-orcid":false,"given":"Andrea","family":"Downing","sequence":"additional","affiliation":[{"name":"The Light Collective  Eugene Oregon USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0107-2984","authenticated-orcid":false,"given":"Stefaan","family":"Verhulst","sequence":"additional","affiliation":[{"name":"The GovLab New York University  New York New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8559-5645","authenticated-orcid":false,"given":"Maryam","family":"Lustberg","sequence":"additional","affiliation":[{"name":"Yale Cancer Center Yale University School of Medicine  New Haven Connecticut USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,11]]},"reference":[{"key":"e_1_2_14_2_1","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-92612-0","volume-title":"Precision Oncology and Cancer Biomarkers. 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