Evidence map›Paper›PMID 32715331›Full record

ReviewEuropean journal of trauma and emergency surgery : official publication of the European Trauma Society2021

The automaton as a surgeon: the future of artificial intelligence in emergency and general surgery.

Lara Rimmer, Callum Howard, Leonardo Picca, Mohamad Bashir

Abstract readReview
PubMed Publisher
In one paragraph

Review in European journal of trauma and emergency surgery : official publication of the European Trauma Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  10. Antimicrobial Challenge in Acute Care Surgery.Antibiotics (Basel, Switzerland) · 2022
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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

4 authors.

Lara RimmerVascular Surgery Department, Royal Blackburn Teaching Hospital, Haslingden Road, Blackburn, BB23HH, UK. lararimmer@gmail.com.ORCID http://orcid.org/0000-0001-5201-1199
Callum HowardFaculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
Leonardo PiccaFaculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
Mohamad BashirVascular Surgery Department, Royal Blackburn Teaching Hospital, Haslingden Road, Blackburn, BB23HH, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) is a field involving computational simulation of human intelligence processes; these applications of deep learning could have implications in the specialty of emergency surgery (ES). ES is a rapidly advancing area, and this review will outline the most recent advances.

methodsA literature search encompassing the uses of AI in surgery was conducted across large databases (Pubmed, OVID, SCOPUS). Two doctors (LR, CH) both collated relevant papers and appraised them. Papers included were published within the last 5 years, and a "snowball effect" used to collate further relevant literature.

resultsAI has been shown to provide value in predicting surgical outcomes and giving personalised patient risks based on inputted data. Further to this, image recognition technology within AI has showed success in fracture identification and breast cancer diagnosis. Regarding theatre presence, supervised robots have carried out suturing and anastomosis of bowel in controlled environments to a high standard.

conclusionAI has potential for integration across surgical services, from diagnosis to treatment, and aiding the surgeon in key decision-making for risks per patient. Fully automated surgery may be the future, but at present, AI needs human supervision.

Indexed as

Artificial IntelligenceAutomationSurgical Procedures, OperativeForecastingHumansSurgeonsArtificial intelligence (AI)Big dataEmergency surgery (ES)Machine learning

Identifiers

PMID32715331

What OpenQuestion holds

Textmetadata
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.