ReviewDiscover oncology2025
The current landscape of artificial intelligence in computational histopathology for cancer diagnosis.
Review in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Decoding cancer with artificial intelligence: Transforming research, diagnosis, and therapy with future insights.Translational oncology · 2026Review
- Detection of basal cell carcinoma on whole-slide images from Mohs micrographic surgery using weakly supervised learning.JAAD international · 2026Article
- Tumours of the duodenum and the ampulla of Vater: An overview of the key criteria for their classification, pathogenesis, and molecular assessment.Virchows Archiv : an international journal of pathology · 2026Review
- Association of Maintenance Therapy Strategies with Survival Following First-Line Bevacizumab-Based Combination Chemotherapy in Metastatic Colorectal Cancer: A Single-Center Retrospective Observational Study.Journal of clinical medicine · 2026Article
- Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.Histochemistry and cell biology · 2026Review
- Benchmarking multiple instance learning architectures from patches to pathology for prostate cancer detection and grading using attention-based weak supervision.Scientific reports · 2026Article
- Manifold-guided SMOTified dual-channel conditional GAN improving highly-imbalanced biomedical data classification.Discover oncology · 2026Article
- Impact of Transfer Learning on Convolutional Neural Networks for Odontogenic Tumor Diagnosis.Head and neck pathology · 2026Article
- [Artificial intelligence in diagnostics-a pathology perspective].Pathologie (Heidelberg, Germany) · 2026Review
- PatchSight-ImmuneMap-LifeSpan as a unified AI framework for breast cancer diagnosis, immune profiling and prognostic prediction.Discover oncology · 2026Article
- Uncertainty-aware and causal test-time adaptive foundation model for robust colorectal cancer pathology diagnosis.NPJ digital medicine · 2025Article
- Review
- Cross-platform multi-cancer histopathology classification using local-window vision transformers.Scientific reports · 2025Article
- Liver Semantic Segmentation Method Based on Multi-Channel Feature Extraction and Cross Fusion.Bioengineering (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) marks a frontier in histopathologic analysis shift towards the clinic, becoming a mainstream choice to interpret histological images. Surveying studies assessing AI applications in histopathology from 2013 to 2024, we review key methods (including supervised, unsupervised, weakly supervised and transfer learning) in deep learning-based pattern recognition in computational histopathology for diagnostic and prognostic purposes. Deep learning methods also showed utility in identifying a wide range of genetic mutations and standard pathology biomarkers from routine histology. This survey of 41 primary studies also encompasses key regions of AI applicability in histopathology in a multi-cancer review while marking prospects to introduce AI into the clinical setting with key examples including Swarm Learning and Data Fusion.
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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.