Cognitive biases in AI-assisted medical decision making: A structured review as a primer for veterinary and human pathology.
TL;DR
This review aims to identify cognitive biases and their modes of manifestation in human-computer interaction within pathology and to extrapolate potential manifestations of biases not yet explored in this domain but reported in HCIs across other medical specialties.
Abstract
Cognitive biases are systematic patterns of error in human thinking. While artificial intelligence (AI)-based decision support systems offer great potential to enhance diagnostic accuracy and efficiency in pathology, collaboration between medical professionals and AI can also introduce or amplify these biases. This review aims to identify cognitive biases and their modes of manifestation in human-computer interaction (HCI) within pathology and to extrapolate potential manifestations of biases not yet explored in this domain but reported in HCIs across other medical specialties. We conducted a structured literature review (19 August 2025) across the ACM, IEEE, and PubMed databases. Studies were eligible if they operationalized cognitive biases during expert-machine interaction in diagnostic decision-making using qualitative or quantitative methods, including review articles citing primary studies that met these criteria. The final corpus comprised 24 studies (8 primary studies and 16 review articles). From these review articles, 18 additional primary studies were extracted and used in their place solely for analysis purposes. A narrative synthesis of the identified primary research revealed 12 cognitive biases reported in AI-assisted medical decision-making, with only one study originating from pathology. For each bias, definitions and hypothetical pathology-specific examples were derived. This review is intended as a primer for the veterinary and human pathology communities and strives to contribute to the safe and effective integration of AI into diagnostic practice.