Background: Novel products applying artificial intelligence (AI)-based approaches to digital pathology images have consistently emerged onto the commercial market, touting improvements in diagnostic accuracy, workflow efficiency, and treatment selection. However, publicly available information on these products can be variable, with few sources to obtain independent evidence. Methods: Our objective was to identify and assess the public evidence on AI-based products for digital pathology. We compared key features of products on the European Economic Area/Great Britain (EEA/GB) markets, including their regulatory approval, intended use, and published validation studies. We included products that used haematoxylin and eosin (H&E)-stained tissue images as input, applied an AI-based method to support image interpretation, and received regulatory approval by September 2023. Results: We identified 26 AI-based products that met our inclusion criteria. The majority (73%) were focused on breast pathology or uropathology, and their primary function was tumour or feature detection. Of the 26 products, 24 had received regulatory approval via the self-certification route as General in vitro diagnostic (IVD) medical devices, which does not require independent review by a conformity assessment body. Furthermore, only 10 of the products (38%) were associated with peer-reviewed scientific publications describing their development and internal validation, while 11 products (42%) had peer-reviewed publications describing external validation (i.e., testing on data from a source distinct to that used in development). Conclusions: The availability of public information on new products for digital pathology is struggling to keep up with the rapid pace of development. To support transparency, we gathered available public evidence on regulatory-approved AI products into an online register: https://resources.npic.uk/AI/ProductRegister. We anticipate this will provide an accessible resource on novel devices and support decisions on which products could bring benefit to patients.

The authors have declared no competing interest.

All authors are funded by the National Pathology Imaging Co-operative (NPIC). NPIC (project no. 104687) is supported by a 50m GBP investment from the Data to Early Diagnosis and Precision Medicine strand of the Industrial Strategy Challenge Fund, managed and delivered by UK Research and Innovation (UKRI). The funders had no role in the study design, data collection, analysis, or writing of the manuscript.

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