Evidence map›Paper›PMID 36046492›Full record

ArticleFrontline gastroenterology2022

Survey on the perceptions of UK gastroenterologists and endoscopists to artificial intelligence.

Rawen Kader, Rebecca F Baggaley, Mohamed Hussein, Omer F Ahmad, Nisha Patel, Gareth Corbett, Sunil Dolwani, Danail Stoyanov, Laurence B Lovat

Open access · hybridAbstract read
In one paragraph

Article in Frontline gastroenterology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.4field-weighted citation impact, top 9% of its field
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, 23 citations in OpenAlex.

  1. Article
  2. Article
  3. Ethical and Legal Implications of Implementing AI in Gastrointestinal Endoscopy.Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society · 2026
    Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. Review
  11. Human-artificial intelligence interaction in gastrointestinal endoscopy.World journal of gastrointestinal endoscopy · 2024
    Review
  12. Article
  13. 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

9 authors at 6 institutions in 1 country.

Rawen KaderDivision of Surgery and Interventional Sciences, University College London, London, UK.ORCID 0000-0001-9133-0838
Rebecca F BaggaleyDepartment of Respiratory Infections, University of Leicester, Leicester, UK.
Mohamed HusseinDivision of Surgery and Interventional Sciences, University College London, London, UK.
Omer F AhmadWellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, UK.ORCID 0000-0001-6498-481X
Nisha PatelDepartment of Gastroenterology, Imperial College Healthcare NHS Trust, London, UK.
Gareth CorbettDepartment of Gastroenterology, Addenbrooke's Hospital, Cambridge, UK.
Sunil DolwaniDivision of Population Medicine, School of Medicine, Cardiff University, Cardiff, UK.
Danail StoyanovDivision of Surgery and Interventional Sciences, University College London, London, UK.
Laurence B LovatDivision of Surgery and Interventional Sciences, University College London, London, UK.
University College London Hospitals NHS Foundation Trust · GBAddenbrooke's Hospital · GBCardiff University · GBImperial College Healthcare NHS Trust · GBUniversity of Leicester · GBWellcome / EPSRC Centre for Interventional and Surgical Sciences · GB

Funding

Wellcome Trust
6 · The paper itself

Abstract

Background and aims: With the potential integration of artificial intelligence (AI) into clinical practice, it is essential to understand end users' perception of this novel technology. The aim of this study, which was endorsed by the British Society of Gastroenterology (BSG), was to evaluate the UK gastroenterology and endoscopy communities' views on AI. Methods: An online survey was developed and disseminated to gastroenterologists and endoscopists across the UK. Results: One hundred four participants completed the survey. Quality improvement in endoscopy (97%) and better endoscopic diagnosis (92%) were perceived as the most beneficial applications of AI to clinical practice. The most significant challenges were accountability for incorrect diagnoses (85%) and potential bias of algorithms (82%). A lack of guidelines (92%) was identified as the greatest barrier to adopting AI in routine clinical practice. Participants identified real-time endoscopic image diagnosis (95%) as a research priority for AI, while the most perceived significant barriers to AI research were funding (82%) and the availability of annotated data (76%). Participants consider the priorities for the BSG AI Task Force to be identifying research priorities (96%), guidelines for adopting AI devices in clinical practice (93%) and supporting the delivery of multicentre clinical trials (91%). Conclusion: This survey has identified views from the UK gastroenterology and endoscopy community regarding AI in clinical practice and research, and identified priorities for the newly formed BSG AI Task Force.

Indexed as

computerised image analysisendoscopy

Identifiers

PMID36046492
PMCPMC9380773
OpenAlexW4206165788

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.