Evaluating the pathological and clinical implications of errors made by an artificial intelligence colon biopsy screening tool.

Journal: BMJ open gastroenterology
PMID: 39762071

Abstract

OBJECTIVE: Artificial intelligence (AI) tools for histological diagnosis offer great potential to healthcare, yet failure to understand their clinical context is delaying adoption. IGUANA (Interpretable Gland-Graphs using a Neural Aggregator) is an AI algorithm that can effectively classify colonic biopsies into normal versus abnormal categories, designed to automatically report normal cases. We performed a retrospective pathological and clinical review of the errors made by IGUANA.

Authors

  • Harriet Evans
    Histopathology Department, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.
  • Naveen Sivakumar
    Department of Colorectal and General Surgery, George Eliot Hospital NHS Trust, Nuneaton, England, UK.
  • Shivam Bhanderi
    Department of Colorectal and General Surgery, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.
  • Simon Graham
    Mathematics for Real World Systems Centre for Doctoral Training, University of Warwick, Coventry, CV4 7AL, UK; Department of Computer Science, University of Warwick, UK. Electronic address: s.graham.1@warwick.ac.uk.
  • David Snead
    Department of Pathology, University Hospitals Coventry and Warwickshire, Coventry, UK.
  • Abhilasha Patel
    Department of Colorectal and General Surgery, University Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.
  • Andrew Robinson
    Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill, North Carolina, 27599, USA.

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