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 and target areas for AI research: Literature & Research Priorities, Discovery, Clinical Trial, Implementation and Post-Implementation & Guidelines. Guidelines useful at multiple stages of research and those currently in development were also highlighted. Summary tables with links to guidelines for these groups were developed, to assist those working in cancer AI research with complete reporting of research. Issues with replication and research waste are recognised problems in AI research. Reporting guidelines can be used as templates to ensure the essential information needed to replicate research is included within journal articles and abstracts. Reporting guidelines are available and useful for many study types, but greater awareness is needed to encourage researchers to utilise them and for journals to adopt them. This review and summary of resources highlights guidance to researchers, aiming to improve completeness of reporting.

Every so often, a technology with the potential to disrupt clinical practice emerges and the medical literature explodes with new studies. These seismic events present a challenge to the peer review process because many reviewers and editorial board members may be unfamiliar with how to evaluate them. Complicating matters, early adopters and thought leaders may not use consistent terminology, may not report results similarly, or may not appreciate fully the potential for inaccurate conclusions based on interpretation errors.

As of June 2019, more than 30 artificial intelligence (AI) algorithms have been approved by the US Food and Drug Administration (including those for the detection of diabetic retinopathy, stroke, brain hemorrhage and atrial fibrillation)1 and over 300 clinical trials have been registered at ClinicalTrials.gov These algorithms have the potential to transform healthcare, by offering earlier and more accurate diagnoses, providing novel insights for the understanding of diseases, enabling faster and more efficient service delivery and making medical care more available to those who really need it. Optimal reporting is key for evaluating the clinical utility of algorithms, for informing health policy and evidence-based recommendations and for preventing research waste2. Most AI interventions thus far, particularly diagnostic algorithms, have been evaluated only in the context of diagnostic accuracy. Although this initial validation stage is important, a demonstration of good diagnostic accuracy does not necessarily translate to improved patient outcomes.