Navigating the reporting guideline environment for computational pathology: A review
Clare McGenity, Darren Treanor

TL;DR
This review identifies and summarizes over 70 reporting guidelines for computational pathology AI research, aiming to improve research transparency, reproducibility, and reduce waste by guiding researchers and journals.
Contribution
It provides a comprehensive, categorized summary of existing reporting guidelines applicable to various stages and types of AI research in pathology.
Findings
Over 70 relevant reporting resources identified
Guidelines categorized into key research stages and areas
Summary tables created for easy access and use
Abstract
The application of new artificial intelligence (AI) discoveries is transforming healthcare research. However, the standards of reporting are variable in this still evolving field, leading to potential research waste. The aim of this work is to highlight resources and reporting guidelines available to researchers working in computational pathology. The EQUATOR Network library of reporting guidelines and extensions was systematically searched up to August 2022 to identify applicable resources. Inclusion and exclusion criteria were used and guidance was screened for utility at different stages of research and for a range of study types. Items were compiled to create a summary for easy identification of useful resources and guidance. Over 70 published resources applicable to pathology AI research were identified. Guidelines were divided into key categories, reflecting current study types…
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Taxonomy
TopicsArtificial Intelligence in Healthcare and Education · Meta-analysis and systematic reviews · Radiomics and Machine Learning in Medical Imaging
MethodsLib
