Learning Health Systems provide a glide path to safe landing for AI in health.

Curcin, Vasa and Delaney, Brendan and Alkhatib, Ahmad and Cockburn, Neil and Dann, Olivia and Kostopoulou, Olga and Leightley, Daniel and Maddocks, Matthew and Modgil, Sanjay and Nirantharakumar, Krishnarajah and Scott, Philip and Wolfe, Ingrid and Zhang, Kelly and Friedman, Charles (2025) Learning Health Systems provide a glide path to safe landing for AI in health. Artificial intelligence in medicine, 173. ISSN 1873-2860

Text Scott, Philip (2025) Learning Health Systems provide a glide path.pdf - Published Version Available under License CC-BY Creative Commons Attribution. Download (867kB)

Abstract

Artificial Intelligence (AI) holds significant promise for healthcare but often struggles to transition from development to clinical integration. This paper argues that Learning Health Systems (LHS)-socio-technical ecosystems designed for continuous data-driven improvement-provide a potential "glide path" for safe, sustainable AI deployment. Just as modern aviation depends on instrument landing systems, the safe and effective integration of AI into healthcare requires the socio-technical infrastructure of LHSs, that enable iterative development and monitoring of AI tools, integrating clinical, technical, and ethical considerations through stakeholder collaboration. They address key challenges in AI implementation, including model generalizability, workflow integration, and transparency, by embedding co-creation, real-world evaluation, and continuous learning into care processes. Unlike static deployments, LHSs support the dynamic evolution of AI systems, incorporating feedback and recalibration to mitigate performance drift and bias. Moreover, they embed governance and regulatory functions-clarifying accountability, supporting data and model provenance, and upholding FAIR (Findable, Accessible, Interoperable, Reusable) principles. LHSs also promote "human-in-the-loop" safety through structured studies of human-AI interaction and shared decision-making. The paper outlines practical steps to align AI with LHS frameworks, including investment in data infrastructure, continuous model monitoring, and fostering a learning culture. Embedding AI in LHSs transforms implementation from a one-time event into a sustained, evidence-based learning process that aligns innovation with clinical realities, ultimately advancing patient care, health equity, and system resilience. The arguments build on insights from an international workshop hosted in 2025, offering a strategic vision for the future of AI in healthcare. [Abstract copyright: Copyright © 2025. Published by Elsevier B.V.]

Item Type:	Article
Additional Information:	** From PubMed via Jisc Publications Router ** History: received 26-07-2025; revised 27-12-2025; accepted 28-12-2025.
Uncontrolled Keywords:	Implementation science, Learning Health Systems, Health data science, Artificial Intelligence
Subjects:	Q Science > QA Mathematics > QA76 Computer software R Medicine > RA Public aspects of medicine
Divisions:	Institutes and Academies > Institute of Management and Health > Business, Finance and Management
Identification Number:	https://doi.org/10.1016/j.artmed.2025.103346
SWORD Depositor:	JISC Publications Router
Depositing User:	JISC Publications Router
Date Deposited:	29 Jan 2026 10:06
Last Modified:	30 Jan 2026 12:22
URI:	https://repository.uwtsd.ac.uk/id/eprint/4102

Administrator Actions (login required)

Edit Item - Repository Staff Only