  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.

Computers and Society;Artificial Intelligence;Human-Computer Interaction