Evidence map›Paper›PMID 41272076›Full record

ReviewNPJ digital medicine2025

A surgical approach to building impactful artificial intelligence.

Simon C Williams, Danyal Z Khan, Sophia Bano, Ann Blandford, Matthew J Clarkson, Enrico Costanza, Evangelos B Mazomenos, Danail Stoyanov, Peter McCulloch, Hani J Marcus

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review 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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. 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

10 authors.

Simon C Williams *National Hospital for Neurology and Neurosurgery, London, UK. rmapwil@ucl.ac.uk.
Danyal Z Khan *National Hospital for Neurology and Neurosurgery, London, UK.
Sophia BanoThe UCL Hawkes Institute, University College London, London, UK.
Ann BlandfordThe UCL Hawkes Institute, University College London, London, UK.
Matthew J ClarksonThe UCL Hawkes Institute, University College London, London, UK.
Enrico CostanzaUniversity College London Interaction Centre, University College London, London, UK.
Evangelos B MazomenosThe UCL Hawkes Institute, University College London, London, UK.
Danail StoyanovThe UCL Hawkes Institute, University College London, London, UK.
Peter McCullochNuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK.
Hani J MarcusNational Hospital for Neurology and Neurosurgery, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Two recent studies in surgical AI video analysis show divergent outcomes based on user expertise and AI explainability. While both demonstrate improved accuracy with AI-assistance, differences in trust and transparency critically shaped impact. This commentary explores human-computer interaction factors such as trust, usability, and explainability, and argues for structured evaluation frameworks to ensure effective, safe integration of AI into surgical practice.

Identifiers

PMID41272076
PMCPMC12639104

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.