Evidence map›Paper›PMID 41786868›Full record

ArticleNPJ digital medicine2026

Comparing artificial intelligence and healthcare professional performance in surgical and interventional video analysis: a systematic review and meta-analysis.

Amir Rafati Fard, Simon C Williams, Kieran J Smith, Jasneet K Dhaliwal, Tomas Ferreira, Adrito Das, Joachim Starup-Hansen, John G Hanrahan, Chan Hee Koh, Danyal Z Khan and 2 more

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Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

12 authors.

Amir Rafati Fard *School of Clinical Medicine, University of Cambridge, Cambridge, UK. amir.fard@ucl.ac.uk.
Simon C Williams *UCL Queen Square Institute of Neurology, University College London, London, UK.
Kieran J SmithSchool of Clinical Medicine, University of Cambridge, Cambridge, UK.
Jasneet K DhaliwalUniversity College London Medical School, London, UK.
Tomas FerreiraBristol Medical School, University of Bristol, Bristol, UK.
Adrito DasUCL Hawkes Institute, University College London, London, UK.
Joachim Starup-HansenUCL Queen Square Institute of Neurology, University College London, London, UK.
John G HanrahanUCL Queen Square Institute of Neurology, University College London, London, UK.
Chan Hee KohUCL Queen Square Institute of Neurology, University College London, London, UK.
Danyal Z KhanUCL Queen Square Institute of Neurology, University College London, London, UK.
Danail StoyanovUCL Hawkes Institute, University College London, London, UK.
Hani J MarcusUCL Queen Square Institute of Neurology, University College London, London, UK. h.marcus@ucl.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This systematic review and meta-analysis examines the design of studies comparing the performance of artificial intelligence (AI) with that of healthcare professionals in the analysis of videos from surgical and interventional procedures, and quantitatively evaluates the performance of AI, unassisted healthcare professionals, and AI-assisted healthcare professionals. From the 37,956 studies identified, 146 were included, with 76 providing sufficient information for inclusion in our exploratory meta-analysis. AI had significantly greater sensitivity and comparable specificity compared to unassisted healthcare professionals at their respective peak performance levels, with a relative risk of 1.12 (95% CI 1.07-1.19, p < 0.001) and 1.04 (95% CI 0.98-1.10, p = 0.224), respectively. AI-assisted healthcare professionals had significantly greater sensitivity and specificity compared to unassisted healthcare professionals across all levels of expertise, with a relative risk of 1.18 (95% CI 1.12-1.25, p < 0.001) and 1.05 (95% CI 1.02-1.08, p < 0.001), respectively. There was no significant difference in sensitivity and specificity of AI-assisted expert healthcare professionals versus AI, with a relative risk of 0.99 (95% CI 0.95-1.04, p = 0.787) and 1.03 (95% CI 0.97-1.08, p = 0.395), respectively. Whilst most studies to date have evaluated AI head-to-head against unassisted healthcare professionals, fewer studies examined AI as an assistive tool, despite the real-world integration of AI more likely to involve assistance than autonomy.

Identifiers

PMID41786868
PMCPMC13086884

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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.