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

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.

Methods

False negative (FN) errors were the primary focus due to the greatest propensity for harm. Pathological evaluation involved assessment of whole slide image (WSI) quality, precise diagnoses for each missed entity and identification of factors impeding diagnosis. Clinical evaluation scored t

SUBMITTER: Evans H 

PROVIDER: S-EPMC11749196 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature