New articles for Veterinary pathology We introduce a 9-point checklist designed to improve report quality for research using artificial intelligence (AI)-based automated image analysis (AIA). As AI tools became more widely used in pathology-based studies, 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 the key methodologies to be included in the manuscript. These include dataset creation, model training, performance assessment, and interaction with AI systems. The aim is to support clear communication of methods and reduce cognitive and algorithmic bias.
“Transparent reporting is important for translating reproducibility and AI tools into everyday pathological workflows,” the author writes. They emphasize that availability of training datasets, source code, and supporting data such as model weights are essential for meaningful validation and wider applications.
The guidelines are intended to assist authors, reviewers and editors, and are particularly useful for submissions Veterinary pathology A future special issue of AI.
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Journal Reference:
Bertram, California, et al. (2025). Reporting guidelines for manuscripts using automated image analysis based on artificial intelligence in veterinary pathology. Veterinary pathology. doi.org/10.1177/03009858251344320
