mlsanityData to 14 Oct 2026

Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy

arXiv 2306.07999 ↗First posted 13 Jun 2023physics.med-ph cs.AI cs.CVcited by 9

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Abstract (arXiv)

Ensuring diagnostic performance of AI models before clinical use is key to the safe and successful adoption of these technologies. Studies reporting AI applied to digital pathology images for diagnostic purposes have rapidly increased in number in recent years. The aim of this work is to provide an overview of the diagnostic accuracy of AI in digital pathology images from all areas of pathology. This systematic review and meta-analysis included diagnostic accuracy studies using any type of artificial intelligence applied to whole slide images (WSIs) in any disease type. The reference standard was diagnosis through histopathological assessment and / or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. We identified 2976 studies, of which 100 were included in the review and 48 in the full meta-analysis. 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. 100 studies were identified for inclusion, equating to over 152,000 whole slide images (WSIs) and representing many disease types. Of these, 48 studies were included in the meta-analysis. 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) for AI. There was substantial heterogeneity in study design and all 100 studies identified for inclusion had at least one area at high or unclear risk of bias. This review provides a broad overview of AI performance across applications in whole slide imaging. However, there is huge variability in study design and available performance data, with details around the conduct of the study and make up of the datasets frequently missing. Overall, AI offers good accuracy when applied to WSIs but requires more rigorous evaluation of its performance.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 15 of its 150 references that are in the map. First posted on arXiv 13 Jun 2023.

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15 of 150 references.

References · 15

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3 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 3.

9 papers.

  1. 2024A Review on Generative AI Models for Synthetic Medical Text, Time Series, and Longitudinal Datacited by 10
  2. 2024A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Modelscited by 9
  3. 2025MorphGen: Morphology-Guided Representation Learning for Robust Single-Domain Generalization in Histopathological Cancer Classificationcited by 2
  4. 2025CellMamba: Adaptive Mamba for Accurate and Efficient Cell Detectioncited by 1
  5. 2026Patch-to-Global: Random Patch Diffusion for Globally Consistent Megapixel Artifact Inpainting in Whole Slide Imagescited by 1
  6. 2025From Explainable to Explained AI: Ideas for Falsifying and Quantifying Explanationscited by 0
  7. 2025PySlyde: A Lightweight, Open-Source Toolkit for Pathology Preprocessingcited by 0
  8. 2026NucEval: A Robust Evaluation Framework for Nuclear Instance Segmentationcited by 0
  9. 2026Explainable Artificial Intelligence (XAI) in Computational Pathology: Definitions, Taxonomy, and Recommendationscited by 0