Evidence map›Paper›PMID 39430867›Full record

ArticleBMJ surgery, interventions, & health technologies2024

Prospective randomized evaluation of the sustained impact of assistive artificial intelligence on anesthetists' ultrasound scanning for regional anesthesia.

Chao-Ying Kowa, Megan Morecroft, Alan J R Macfarlane, David Burckett-St Laurent, Amit Pawa, Simeon West, Steve Margetts, Nat Haslam, Toby Ashken, Maria Paz Sebastian and 5 more

Abstract read
In one paragraph

Article in BMJ surgery, interventions, & health technologies, 2024. 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. Review
  2. Review
  3. Review
  4. Reshaping Anesthesia with Artificial Intelligence: From Concept to Reality.Anesthesia and critical care (Houston, Tex.) · 2025
    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

15 authors.

Chao-Ying KowaDepartment of Anaesthesia, The Royal London Hospital, London, UK.
Megan MorecroftFaculty of Medicine, Health & Life Sciences, University of Swansea, Swansea, UK.
Alan J R MacfarlaneDepartment of Anaesthesia, Glasgow Royal Infirmary, Glasgow, UK.
David Burckett-St LaurentDepartment of Anaesthesia, Royal Cornwall Hospitals NHS Trust, Truro, UK.
Amit PawaDepartment of Medicine and Perioperative Medicine, Guy's and St Thomas' NHS Foundation Trust, London, UK.
Simeon WestDepartment of Anaesthesia, University College London Hospitals NHS Foundation Trust, London, UK.
Steve MargettsIntelligent Ultrasound, Cardiff, UK.
Nat HaslamDepartment of Anaesthesia, South Tyneside and Sunderland NHS Foundation Trust, South Shields, UK.
Toby AshkenDepartment of Anaesthesia, University College London Hospitals NHS Foundation Trust, London, UK.
Maria Paz SebastianDepartment of Anaesthetics, Royal National Orthopaedic Hospital NHS Trust, Stanmore, UK.
Athmaja ThottungalDepartment of Anaesthesia and Pain Management, East Kent Hospitals University NHS Foundation Trust, Canterbury, UK.
Jono WomackDepartment of Anaesthesia, Royal Victoria Infirmary, Newcastle upon Tyne, UK.
Julia Alison NobleInstitute of Biomedical Engineering, University of Oxford, Oxford, UK.
Helen HighamNuffield Department of Clinical Anaesthesia, University of Oxford, Oxford, UK.
James S BownessDepartment of Anaesthesia, University College London Hospitals NHS Foundation Trust, London, UK.ORCID 0000-0002-8665-1984

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Ultrasound-guided regional anesthesia (UGRA) relies on acquiring and interpreting an appropriate view of sonoanatomy. Artificial intelligence (AI) has the potential to aid this by applying a color overlay to key sonoanatomical structures.The primary aim was to determine whether an AI-generated color overlay was associated with a difference in participants' ability to identify an appropriate block view over a 2-month period after a standardized teaching session (as judged by a blinded assessor). Secondary outcomes included the ability to identify an appropriate block view (unblinded assessor), global rating score and participant confidence scores. Design: Randomized, partially blinded, prospective cross-over study. Setting: Simulation scans on healthy volunteers. Initial assessments on 29 November 2022 and 30 November 2022, with follow-up on 25 January 2023 - 27 January 2023. Participants: 57 junior anesthetists undertook initial assessments and 51 (89.47%) returned at 2 months. Intervention: Participants performed ultrasound scans for six peripheral nerve blocks, with AI assistance randomized to half of the blocks. Cross-over assignment was employed for 2 months. Main outcome measures: Blinded experts assessed whether the block view acquired was acceptable (yes/no). Unblinded experts also assessed this parameter and provided a global performance rating (0-100). Participants reported scan confidence (0-100). Results: AI assistance was associated with a higher rate of appropriate block view acquisition in both blinded and unblinded assessments (p=0.02 and <0.01, respectively). Participant confidence and expert rating scores were superior throughout (all p<0.01). Conclusions: Assistive AI was associated with superior ultrasound scanning performance 2 months after formal teaching. It may aid application of sonoanatomical knowledge and skills gained in teaching, to support delivery of UGRA beyond the immediate post-teaching period. Trial registration number: www.clinicaltrials.govNCT05583032.

Indexed as

Device EvaluationDevicesTechnology

Identifiers

PMID39430867
PMCPMC11487881

What OpenQuestion holds

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Registered trials

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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.