Evidence map›Paper›PMID 42064399›Full record

ReviewBJR artificial intelligence2024

Adoption, orchestration, and deployment of artificial intelligence within the National Health Service-facilitators and barriers: an expert roundtable discussion.

Gerald Lip, Alex Novak, Mathias Goyen, Katherine Boylan, Amrita Kumar

Abstract readReview
In one paragraph

Review in BJR artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
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

5 authors.

Gerald LipUniversity of Aberdeen, Aberdeen, AB25 2ZD, United Kingdom.ORCID https://orcid.org/0000-0002-7867-5468
Alex NovakEmergency Medicine Research Oxford (EMROx), Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 9DU, United Kingdom.ORCID https://orcid.org/0000-0002-5880-8235
Mathias GoyenGE HealthCare, Peter-Müller-Str. 24-26, D-40468, Düsseldorf, Germany.ORCID https://orcid.org/0000-0003-1102-4879
Katherine BoylanManchester University NHS Foundation Trust (MFT), Manchester, M13 9WL, United Kingdom.ORCID https://orcid.org/0000-0003-2478-7664
Amrita KumarFrimley Health NHS Foundation Trust, Frimley, SL5 7SG, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clinical care, workflow, and patient outcomes within National Health Service (NHS) radiology services will potentially benefit significantly in coming years with the integration of artificial intelligence (AI) into various specialty areas. This article summarizes an expert roundtable discussion, convened by the British Institute of Radiology, comprised experts from clinical, NHS management and industry backgrounds. Facilitators and barriers to both implementation and integration of AI platforms into daily practice are discussed alongside examples including AI in emergency care and breast screening. Issues addressed include the need to generate more real-world evidence that helps developers and service providers to determine the optimal way for patients to reap the benefits of AI with faster reporting turnaround time, faster diagnoses, and treatment times, for example. To ensure the effective and timely wide-scale implementation, adoption requires a national policy drive and national level coordination but specific, local needs of NHS Trusts and any particular service within Trusts will also need to be met. By embedding AI-capabilities into scanners, ultrasound-devices, X-ray equipment, and Picture archiving and communication system (PACS), local services will benefit, while AI applications can streamline workflows across and between departments and potentially Trusts. AI predictive analytics and systems engineering can be used to better manage patient volume and movement. In the short term, education, training, and confidence in adapting and adopting AI in radiology require dedicated local, national, and governmental effort but the future benefits promise to justify the means.

Indexed as

adaptionadoptionartifical intelligencecancereducationhealthcareNHSplatformsradiologytraining

Identifiers

PMID42064399
PMCPMC13045691

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.