ArticleDigital health
A system-level analysis of challenges and strategies for scaling artificial intelligence in healthcare: A qualitative study of the NHS AI lab.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Despite Artificial Intelligence's (AI) promise in healthcare, achieving widespread and lasting adoption remains a global challenge. While pilot studies show positive impacts, the reasons for limited uptake in practice are underexplored. This paper examines challenges hindering AI scaling in healthcare and develops a system-level explanation of why these challenges persist, alongside strategies to address them. Methods: As part of a wider evaluation of England's National Health Service (NHS) AI Lab, we conducted in-depth semi-structured interviews, documentary analysis, and observations of related meetings and events between April 2024 and April 2025. Data were analyzed using reflexive thematic analysis, followed by a higher-level conceptual synthesis. Results: We identified 15 interrelated challenges spanning market forces, healthcare adopter dynamics, and macro-environment challenges. Challenges included unclear pathways from pilots to adoption, fragmented regulatory and evaluation processes, limited funding for scaling, procurement and organizational capability gaps, uneven digital maturity, unclear partnership models, uncertain returns on investment, and difficulties navigating fragmented healthcare systems. Rather than operating as discrete issues, these challenges reflect four underlying and interdependent system-level mechanisms: fragmented innovation pathways, fragmented governance and lack of system orchestration, organizational transformation deficits, and market and incentive misalignment. Together, these mechanisms help explain why AI innovations frequently struggle to progress from pilot experimentation to routine and system-wide adoption. Conclusions: Our findings shift the focus from isolated challenges to the system-level dynamics through which challenges to AI scaling emerge, interact, and persist. The study contributes an empirically grounded explanation of the pilot-to-scale gap and highlights how governance, organizational, market, and innovation dynamics jointly constrain scaling. Building on these findings, we propose a set of coordinated strategic responses that emphasize system orchestration, organizational transformation, coherent innovation pathways, and continuous evaluation across the AI lifecycle.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.