ReviewNPJ digital medicine2024
Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy.
Review in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 132 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
132 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Stage-stratified benefits of AI-radiomics PET in early Alzheimer's disease: a systematic review and meta-analysis.Frontiers in neurologyPooled it
- AI Trustworthiness in the Perioperative Period for Patients with Serious Illness: Scoping Narrative Review.JMIR perioperative medicine · 2026Article
- Artificial intelligence in digital pathology diagnosis and analysis: Technologies, clinical integration, and future prospects (Review).Biomedical reports · 2026Review
- A Generalizable Multimodal Model for Treatment-Stratified Risk and Survival Assessment under Real-World Constraints: A Multi-Center Study of Colorectal Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- AI for Health Care Quality and Patient Safety: Scoping Review of Diagnostic, Predictive, and Decision Support Applications.Journal of medical Internet research · 2026Article
- Integrated Immune Escape in Cervical Cancer: HLA-I Dysfunction and Immune Checkpoint Signaling.International journal of molecular sciences · 2026Review
- When Cancer Research Met Artificial Intelligence: From Machine Learning to Intelligent Oncology.Cancers · 2026Article
- The Role of Computational Models in the Detection of Colorectal Carcinoma and Precancerous Lesions.International journal of molecular sciences · 2026Review
- The benchmark illusion: on the curious underestimation of AI and the uncertainty of its creators' gold standards.EClinicalMedicine · 2026Review
- Generalizability of a biopsy-trained artificial intelligence algorithm to TURP and HoLEP specimens for detection of incidental prostate adenocarcinoma.Diagnostic pathology · 2026Article
- V7-DiVA: a feature-based Deep MIL colorectal whole-slide histopathology research platform with blinded external image-input validation.Journal of translational medicine · 2026Article
- Deep learning-assisted detection of lymph node metastases in bladder cancer.Diagnostic pathology · 2026Article
- Validation, implementation, and impact of an AI model in routine practice for pathologic diagnosis of prostate cancer in an academic medical center.Journal of pathology informatics · 2026Article
- A benchmark study of vision and pathology foundation models for computational pathology.Nature communications · 2026Article
- Deep learning-based assessment of HER2-ultralow expression in breast cancer: a multi-center validation study across primary and metastatic lesions.Virchows Archiv : an international journal of pathology · 2026Article
- Emerging Serological Biomarkers for Autoimmune Hepatitis.Journal of clinical medicine · 2026Review
- Comparison of Large Language Model Performance on the United Kingdom Neurology Specialty Certificate Examination.Cureus · 2026Article
- A Transformer-Residual Network-Integrated Deep Learning Model for Digital Pathology-Based Grading and Risk Stratification of Metabolic Dysfunction-Associated Steatotic Liver Disease-Related Liver Fibrosis.Clinical and translational gastroenterology · 2026Article
- Strengthening Biomarker Research in Canadian Cancer Clinical Trials: A Pathology-Focused White Paper.Current oncology (Toronto, Ont.) · 2026Article
- MHub.ai: A Standardized Platform for Reproducible AI Research in Medical Imaging.Research square · 2026Article
72 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
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.
Identifiers
What OpenQuestion holds
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.