Evidence map›Paper›PMID 39474680›Full record

ArticleAnnals of surgery2026

Artificial Intelligence-Assisted Surgical Scene Recognition: A Comparative Study Among Health Care Professionals.

Simon C Williams, Jinfan Zhou, William R Muirhead, Danyal Z Khan, Chan Hee Koh, Razna Ahmed, Jonathan P Funnell, John G Hanrahan, Alshaymaa Mortada Ali, Shankhaneel Ghosh and 7 more

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
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  7. Review
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

17 authors.

Simon C WilliamsUCL Hawkes Institute, University College London, London, UK.ORCID 0000-0003-1770-1797
Jinfan ZhouUCL Hawkes Institute, University College London, London, UK.
William R MuirheadUCL Hawkes Institute, University College London, London, UK.
Danyal Z KhanUCL Hawkes Institute, University College London, London, UK.
Chan Hee KohUCL Hawkes Institute, University College London, London, UK.
Razna AhmedUCL Hawkes Institute, University College London, London, UK.
Jonathan P FunnellUCL Hawkes Institute, University College London, London, UK.
John G HanrahanUCL Hawkes Institute, University College London, London, UK.
Alshaymaa Mortada AliAin Shams University, Cairo, Egypt.
Shankhaneel GhoshDepartment of General Medicine, Institute of Medical Sciences and SUM Hospital, Bhubaneswar, India.
Tarik SaridoğanDepartment of Emergency Medicine, Bursa City Hospital, Bursa, Turkey.
Alexandra ValetopoulouDepartment of Neurosurgery, Imperial College Healthcare NHS Trust, London, UK.
Patrick GroverVictor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK.
Danail StoyanovUCL Hawkes Institute, University College London, London, UK.
Mary MurphyVictor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK.
Evangelos B MazomenosUCL Hawkes Institute, University College London, London, UK.
Hani J MarcusUCL Hawkes Institute, University College London, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo compare the ability of a deep-learning platform (the MACSSwin-T model) with health care professionals in detecting cerebral aneurysms from operative videos. Secondly, we aimed to compare health care professionals' ability to detect cerebral aneurysms with and without artificial intelligence (AI) assistance.

backgroundModern microscopic surgery enables the capture of operative video data on an unforeseen scale. Advances in computer vision, a branch of AI, have enabled automated analysis of operative video. These advances are likely to benefit clinicians, health care systems, and patients alike, yet such benefits are yet to be realized.

methodsIn a cross-sectional comparative study, neurosurgeons, anesthetists, and operating room nurses, all at varying stages of training and experience, reviewed still frames of aneurysm clipping operations and labeled frames as "aneurysm not in frame" or "aneurysm in frame." Frames then underwent analysis by the AI platform. A second round of data collection was performed, whereby the neurosurgical team had AI assistance. The accuracy of aneurysm detection was calculated for human-only, AI-only, and AI-assisted human groups.

resultsA total of 5154 individual frame reviews were collated from 338 health care professionals. Health care professionals correctly labeled 70% of frames without AI assistance, compared with 78% with AI assistance (odds ratio: 1.77, P < 0.001). Neurosurgical Attendings showed the greatest improvement, from 77% to 92% correct predictions with AI assistance (odds ratio: 4.24, P = 0.003).

conclusionsAI-assisted human performance surpassed both human and AI alone. Notably, across health care professionals surveyed, frame accuracy improved across all subspecialties and experience levels, particularly among the most experienced health care professionals. These results challenge the prevailing notion that AI primarily benefits junior clinicians, highlighting its crucial role throughout the surgical hierarchy as an essential component of modern surgical practice.

Indexed as

Artificial IntelligenceDeep LearningHealth PersonnelIntracranial AneurysmNeurosurgical ProceduresCross-Sectional StudiesHumansVideo Recordinganeurysmartificial intelligencecomputer visionmachine learningneurosurgeryvascular neurosurgery

Identifiers

PMID39474680
PMCPMC13056406

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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.