Artificial Intelligence in Digital Pathology: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy
Open accessAbstract
Ensuring diagnostic performance of artificial intelligence (AI) before introduction into clinical practice is essential. Growing numbers of studies using AI for digital pathology have been reported over recent years. The aim of this work is to examine the diagnostic accuracy of AI in digital pathology images for any disease. This systematic review and meta-analysis included diagnostic accuracy studies using any type of AI applied to whole slide images (WSIs) for any disease. The reference standard was diagnosis by histopathological assessment and/or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. Risk of bias and concerns of applicability were assessed using the QUADAS-2 tool. Data extraction was conducted by two investigators and meta-analysis was performed using a bivariate random effects model, with additional subgroup analyses also performed. Of 2976 identified studies, 100 were included in the review and 48 in the meta-analysis. Studies were from a range of countries, including over 152,000 whole slide images (WSIs), representing many diseases. These studies reported a mean sensitivity of 96.3% (CI 94.1-97.7) and mean specificity of 93.3% (CI 90.5-95.4). There was heterogeneity in study design and 99% of studies identified for inclusion had at least one area at high or unclear risk of bias or applicability concerns. Details on selection of cases, division of model development and validation data and raw performance data were frequently ambiguous or missing. AI is reported as having high diagnostic accuracy in the reported areas but requires more rigorous evaluation of its performance.
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STARD-AI can be applied across a broad range of diagnostic modalities, including imaging, pathology and clinical information in EHRs.
“Use of STARD-AI can aid the comprehensive reporting of research that assesses AI diagnostic accuracy using either single or combined test data and can be applied across a broad range of diagnostic modalities.”
From The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence (2025) · cites this paper as [33]
The Discussion > Findings says STARD-AI could help improve the completeness of reporting in the digital-pathology studies reviewed. It does not say the guideline applies to single or combined test data or across diagnostic modalities such as EHR-based tests. The Discussion mentions imaging and EHRs as examples of AI innovation, not as applications of STARD-AI; no page numbers are available in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredA meta-analysis pooling 100 studies using more than 152,000 whole-slide images reported a mean sensitivity of 96.3% and a mean specificity of 93.3% for pathology AI.
“A recent meta-analysis that pooled 100 studies using more than 152,000 WSI reported a mean sensitivity of 96.3% and a mean specificity of 93.3%.”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [35]
Pathology AI performance varied by subspecialty: gastrointestinal (93%/94%), urologic (95%/96%), and breast (83%/88%).
“Performance varied by subspecialty: gastrointestinal (93%/94%), urologic (95%/96%), and breast (83%/88%) [35].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [35]
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Artificial Intelligence-Based Opportunities in Liver Pathology—A Systematic Review
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Detection of malignant melanoma in H&E-stained images using deep learning techniques
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Exploring Histological Similarities Across Cancers From a Deep Learning Perspective
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MLCD: A Unified Software Package for Cancer Diagnosis
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Flagged first, then by citations
Research works printed but not linked: 1
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Cited by
Sorted by citations
The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence
Sounderajah, V., Guni, A., Liu, X., Collins, G. S., Karthikesalingam, A., Markar, S. R., Golub, R. M., Denniston, A. K., Shetty, S., Moher, D., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03953-8 · Cited by 352 · see citing works
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Bridging imaging-based in vitro methods from biomedical research to regulatory toxicology
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“The performance of the ML/DL model used for image analysis should be accurately measured with problem-specific metrics, some guidance on different approaches and recommendations can be found in (Laine et al. 2021; Maier-Hein et al. 2024; McGenity et al. 2024).”
Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery
Kuno, M., Osumi, H., Udagawa, S., Yoshikawa, K., Ooki, A., Shinozaki, E., Ishikawa, T., Oba, J., Yamaguchi, K., & Sakurada, K. (2025) · Current Oncology · doi:10.3390/curroncol32110588 · Cited by 7 · see citing works
Türkmen Dedeoğlu, A., Yanık, H., Uysal, E., Fikir, G., Değirmenci, E., Türsen, Ü., & Yuyucu Karabulut, Y. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-02057-0
- Authors
- McGenity, C., Clarke, E. L., Jennings, C., Matthews, G., Cartlidge, C., Freduah-Agyemang, H., Stocken, D. D., & Treanor, D.
- Affiliations
- University of Leeds (GB)
- Leeds Teaching Hospitals NHS Trust (GB)
- Linköping University (SE)
- Corresponding author
- McGenity, Clare
- ORCID
- Venue
- npj Digital Medicine
- Published
- 2024-05-04
- Volume
- 7
- Issue
- 1
- Pages
- 114-114
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC11069583
- OpenAlex
- W4396636942
- Cited by
- 290
- References
- 144
- Citations by year
- 2026: 163 · 2025: 118 · 2024: 9
- FWCI
- 9.93
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- Diva portal (Dalarna University Library) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Nuclear Power Institute of China (104687)
- UK Research and Innovation (104687)
- National Institute for Health and Care Research
- Department of Health and Social Care
- University of Leeds
- Medical Research Council (MR/S001530/1)
- Leeds Biomedical Research Centre
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- artificial intelligence, digital pathology, whole slide images, diagnostic accuracy, sensitivity and specificity, histopathology, immunohistochemistry, risk of bias, model validation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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