BibTex format
@article{Curcin:2026:10.1016/j.artmed.2025.103346,
author = {Curcin, V and Delaney, B and Alkhatib, A and Cockburn, N and Dann, O and Kostopoulou, O and Leightley, D and Maddocks, M and Modgil, S and Nirantharakumar, K and Scott, P and Wolfe, I and Zhang, K and Friedman, C},
doi = {10.1016/j.artmed.2025.103346},
journal = {Artif Intell Med},
title = {Learning Health Systems provide a glide path to safe landing for AI in health.},
url = {http://dx.doi.org/10.1016/j.artmed.2025.103346},
volume = {173},
year = {2026}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - 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.
AU - Curcin,V
AU - Delaney,B
AU - Alkhatib,A
AU - Cockburn,N
AU - Dann,O
AU - Kostopoulou,O
AU - Leightley,D
AU - Maddocks,M
AU - Modgil,S
AU - Nirantharakumar,K
AU - Scott,P
AU - Wolfe,I
AU - Zhang,K
AU - Friedman,C
DO - 10.1016/j.artmed.2025.103346
PY - 2026///
TI - Learning Health Systems provide a glide path to safe landing for AI in health.
T2 - Artif Intell Med
UR - http://dx.doi.org/10.1016/j.artmed.2025.103346
UR - https://www.ncbi.nlm.nih.gov/pubmed/41506062
VL - 173
ER -
