Document Type
Article
Publication Date
10-11-2026
Abstract
Healthcare services and data ecosystems remain fragmented, inequitable and misaligned with patient, clinician and public health needs. Three pathways to patient-centred 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-grounded 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-centred AI. AML is defined as continuous learning from real-world individual-level data to generate personalised, population-level insights returning direct benefit to the communities who produce them. A narrative review of PubMed and Google Scholar (2018-2025) was conducted across five thematic domains: AI and machine learning in healthcare; real-world data and evidence; patient-centred 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-oriented rather than static diagnostic approaches. Eligibility was determined through iterative thematic analysis. Eight systemic barriers to patient-centred 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-based 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-stakeholder validation. Future research should address federated AML validation, collective consent mechanisms and alignment of regulatory frameworks with international AI governance standards.
Recommended Citation
Kalogeropoulos, Dimitrios; Barach, Paul; Downing, Andrea; Verhulst, Stefaan; and Lustberg, Maryam, "Advancing Patient-Centred AI With Adaptive Machine Learning" (2026). College of Population Health Faculty Papers. Paper 262.
https://jdc.jefferson.edu/healthpolicyfaculty/262
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
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Language
English
Comments
This article is the author's final published version in Cancer Medicine, Volume 15, Issue 10, October 2026, Article number e72371.
The published version is available at https://doi.org/10.1002/cam4.72371. Copyright © 2026 The Author(s).