A 9-point checklist to improve AI-based image analysis in pathology

A new article in Veterinary Pathology introduces a 9-point checklist designed to improve the reporting quality of studies that use artificial intelligence (AI)-based automated image analysis (AIA). As AI tools become more widely used in pathology-based research, concerns have emerged about the reproducibility and transparency of published findings.

Developed by an interdisciplinary team of veterinary pathologists, machine learning experts, and journal editors, the checklist outlines key methodological details that should be included in manuscripts. These include dataset creation, model training, performance evaluation, and interaction with the AI system. The aim is to support clear communication of methods and reduce cognitive and algorithmic bias.

"Transparent reporting is critical for reproducibility and for translating AI tools into routine pathology workflows," the authors write. They emphasize that the availability of supporting data-such as training datasets, source code, and model weights, is essential for meaningful validation and broader application.

The guidelines are intended to assist authors, reviewers, and editors and will be particularly useful for submissions to Veterinary Pathology's upcoming special issue on AI.