ReviewModern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc2022
Recommendations on compiling test datasets for evaluating artificial intelligence solutions in pathology.
Review in Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 34 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
34 citing papers in PubMed.
- Artificial Intelligence for Diagnostic and Prognostic Support in Breast Cancer: A Literature Overview.Cancers · 2026Review
- Mechanistic Insights into Wildlife Cancer and Conservation Strategies Under the One Health Framework.Veterinary sciences · 2026Review
- Predicting Response to Immune Checkpoint Inhibitors in Melanoma: Emerging Approaches in Digital Pathology, Spatial Profiling and Machine Learning.International journal of molecular sciences · 2026Review
- The SALV-Dataset Registry: An Expertly Curated Digital Clinicopathological Dataset for Salivary Gland Tumor Research and AI-Assisted Diagnostic Tools.Head and neck pathology · 2026Article
- Evaluating the impact of the 'Four Pest-Free Villages' program on mosquito-borne disease control in Zhejiang Province, China: a cross-sectional study on knowledge, attitudes, and practices.Infectious diseases of poverty · 2026Article
- Artificial intelligence‑based quantitative analysis of hepatic fibrosis in carbon tetrachloride-induced mouse model of metabolic dysfunction-associated steatohepatitis.Toxicological research · 2026Article
- Multiparametric cellular and spatial organization in cancer tissue lesions with a streamlined pipeline.Nature biomedical engineering · 2026Article
- Smart Lies and Sharp Eyes: Pragmatic Artificial Intelligence for Cancer Pathology: Promise, Pitfalls, and Access Pathways.Cancers · 2026Review
- Foundation Models in Cancer Pathology: Techniques, Applications, and Future Directions.Research (Washington, D.C.) · 2026Review
- Digital pathology and artificial intelligence in breast and gynecologic oncology: from molecular prediction to multimodal integration.Frontiers in oncology · 2026Review
- An artificial intelligence model of whole-slide pathology specimens differentiating cutaneous high-grade squamous proliferations.Virchows Archiv : an international journal of pathology · 2025Article
- Enhanced metastasis risk prediction in cutaneous squamous cell carcinoma using deep learning and computational histopathology.NPJ precision oncology · 2025Article
- Computational pathology for breast cancer: Where do we stand for prognostic applications?Breast (Edinburgh, Scotland) · 2025Review
- ECP-GAN: Generating Endometrial Cancer Pathology Images and Segmentation Labels via Two-Stage Generative Adversarial Networks.Annals of surgical oncology · 2025Article
- Artificial Intelligence in Placental Pathology: New Diagnostic Imaging Tools in Evolution and in Perspective.Journal of imaging · 2025Review
- Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review.Journal of pathology informatics · 2024Review
- Joining forces for pathology diagnostics with AI assistance: The EMPAIA initiative.Journal of pathology informatics · 2024Review
- Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities.Journal of pathology informatics · 2024Review
- AI drives the assessment of lung cancer microenvironment composition.Journal of pathology informatics · 2024Article
- Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers.International journal of clinical oncology · 2024Review
Corrections and comments
- Erratum issued
Authors and funding
30 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 on compiling 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 pathologists and regulatory agencies 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.