  Artificial intelligence (AI) solutions that automatically extract information
from digital histology images have shown great promise for improving
pathological diagnosis. Prior to routine use, it is important to evaluate their
predictive performance and obtain regulatory approval. This assessment requires
appropriate test datasets. However, compiling such datasets is challenging and
specific recommendations are missing.
  A committee of various stakeholders, including commercial AI developers,
pathologists, and researchers, discussed key aspects and conducted extensive
literature reviews on test datasets in pathology. Here, we summarize the
results and derive general recommendations for the collection of test datasets.
  We address several questions: Which and how many images are needed? How to
deal with low-prevalence subsets? How can potential bias be detected? How
should datasets be reported? What are the regulatory requirements in different
countries?
  The recommendations are intended to help AI developers demonstrate the
utility of their products and to help regulatory agencies and end users verify
reported performance measures. Further research is needed to formulate criteria
for sufficiently representative test datasets so that AI solutions can operate
with less user intervention and better support diagnostic workflows in the
future.
