ArticleNPJ digital medicine2025
A mixed methods formative evaluation of the United Kingdom National Health Service Artificial Intelligence Lab.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed.
- When Markets Shape AI Mental Health Self-Management Tools: Consequences for Serious Mental Illness.JMIR mental health · 2026Article
- Clinical artificial intelligence (AI) in Japan: Regulatory pathways, domain-specific evidence, and its data infrastructure from an international perspective.Global health & medicine · 2026Review
- Large-scale system-level digitalisation initiatives in the National Health Service in England: insights from three national evaluations.NPJ digital medicine · 2026Article
- How artificial intelligence is disrupting the field of medical knowledge and evaluation: a narrative review.BMJ digital health & AI · 2026Review
- Artificial Intelligence in Medication Adherence: A National Assessment of Knowledge, Attitudes, and Perceptions Among Chronic Disease Patients in Jordan.Patient preference and adherence · 2026Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Internationally, health systems are investing in Artificial Intelligence (AI) to improve safety, quality, and efficiency, yet many efforts remain localised and do not progress beyond early development stages. In 2019, National Health Service (NHS) England and the Department of Health and Social Care launched the AI Lab to accelerate safe AI adoption. We conducted a mixed-methods evaluation of the AI Lab, analysing 1021 documents and 85 stakeholder interviews. The AI Lab made important contributions to national AI policy, regulation, and capability building, and positioned the United Kingdom as a global leader in AI deployment for health. Despite progress, implementation and scaling were hindered by shifting objectives, limited capacity, and systemic misalignment with service needs. Some AI technologies demonstrated high return on investment and improved clinical processes. Lessons from the AI Lab highlight critical socio-organisational factors, gaps in scaling support, and the need for sustained coordination to realise the long-term benefits of AI in health and social care systems.
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.