Evidence map›Paper›PMID 41883556›Full record

ArticleDigital health

A clinician's quick‑start guide to implementing digital health innovations in the NHS - with lessons from a UK-deployed AI stroke imaging decision-support software.

Anurup Mukherjee, Sukhi Shergill, Chee Siang Ang

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Anurup MukherjeeClinical Academic Training Office, Kent and Medway Medical School, Canterbury, UK.ORCID https://orcid.org/0009-0007-8742-463X
Sukhi ShergillDepartment of Research, Kent and Medway Medical School, Canterbury, UK.
Chee Siang AngSchool of Computing, Kent and Medway Medical School, Canterbury, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The successful implementation of digital health and artificial intelligence (AI) innovations in the National Health Service (NHS) requires more than technical development. Navigating regulation, generating decision-grade evidence, and meeting clinical safety, information-governance, and interoperability standards are critical steps that frequently delay or prevent adoption. This article presents a practical, implementation-focused roadmap designed to help clinicians, innovators, and healthcare leaders translate policy requirements into real-world NHS deployment. Drawing on guidance from the Medicines and Healthcare products Regulatory Agency (MHRA), the National Institute for Health and Care Excellence (NICE), and NHS Digital, we outline an eight-step pathway covering medical-device classification, value-proposition development, intended-purpose definition, regulatory approval, evidence generation, algorithmic fairness and generalisability, interoperability and information governance, and post-market surveillance. Unlike high-level digital health frameworks, the roadmap specifies minimum artefacts, typical ownership and sign-off responsibilities, and decision points aligned with NHS procurement and clinical governance processes. The roadmap is illustrated through a detailed case study of a UK-deployed AI stroke imaging decision-support software. Its progression from academic development to multi-site NHS deployment demonstrates how early regulatory engagement, robust real-world evaluation, and sustained clinical collaboration can support safe scaling and measurable service improvements, including increased access to reperfusion therapies and reduced inter-hospital transfer times. By distilling complex regulatory and evidence requirements into executable steps, this guide offers a clear route from idea to adoption. It emphasises that aligning regulation, evidence generation, bias mitigation, and interoperability from the outset is essential to sustainable digital health integration within the NHS.

Indexed as

artificial intelligencedigital healthhealth technology assessmentinteroperability and DTACMHRANHS innovationNICE evidence standards framework

Identifiers

PMID41883556
PMCPMC13009856

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.