# Digital pathology & AI

Source: https://onco.cc/technologies/digital-pathology-ai/  
OnCo record `digital-pathology-ai` (Technology). Data CC BY-NC 4.0, attribute "Data from OnCo (onco.cc)"; commercial use needs a licence.

## TL;DR

Scanning microscope slides and letting software measure things a pathologist cannot see, including predictions of who will benefit from a treatment.

## Summary

Whole-slide imaging, scanning glass slides into gigapixel digital images, is now routine in large pathology laboratories. FDA-cleared tools: Paige Prostate (detection), ArteraAI Prostate (2025, first prognostic and predictive AI) and ArteraAI Breast (May 2026, risk stratification in early HR+/HER2- breast cancer). Foundation models (Virchow, UNI, CONCH, Prov-GigaPath) predict molecular status (MSI, HRD, HER2) from H&E alone.

## Fields

- Kind: Technology
- Status: established
- Last checked: 2026-09-04
- Principle: Gigapixel whole-slide images; tile-level self-supervised encoders aggregated to slide-level predictions.
- Strengths: Cheap biomarker from routine slides; Consistent scoring (Ki-67, TILs, HER2)
- Limitations: Scanner and stain domain shift; Explainability; Regulatory pathways for updates

## Sources

- Wikipedia: https://en.wikipedia.org/wiki/Digital_pathology
- Wikipedia: https://en.wikipedia.org/wiki/Digital_pathology

## Connected records

- pairings: [AI pathology → androgen deprivation duration](https://onco.cc/pairings/ai-pathology-to-adt/)
- roadmaps: [AI in oncology roadmap: pattern readers → foundation models → agents in the workflow](https://onco.cc/roadmaps/ai-oncology-roadmap/), [AI in the oncology clinic: from narrow cleared tools to multimodal decision support](https://onco.cc/roadmaps/ai-oncology-clinic/), [Diagnostics roadmap: stains → gene panels → blood tests that decide treatment](https://onco.cc/roadmaps/diagnostics-roadmap/), [Triple-negative breast cancer roadmap: from a remainder defined by three negative tests to immunotherapy, antibody-drug conjugates and the residual disease problem](https://onco.cc/roadmaps/tnbc-roadmap/)
- fronts: [AI & Computation](https://onco.cc/fronts/ai-computation/), [Diagnostics & Biomarkers](https://onco.cc/fronts/diagnostics/)
- drugs: [ArteraAI Breast](https://onco.cc/drugs/artera-ai-breast/), [ArteraAI Prostate](https://onco.cc/drugs/artera-ai-prostate/), [Paige Prostate Detect](https://onco.cc/drugs/paige-prostate/)
- companies: [Artera](https://onco.cc/companies/artera/), [Ataraxis AI](https://onco.cc/companies/ataraxis-ai/), [Digistain](https://onco.cc/companies/digistain/), [HistoWiz](https://onco.cc/companies/histowiz/), [Ibex Medical Analytics](https://onco.cc/companies/ibex-medical-analytics/), [Imagene AI](https://onco.cc/companies/imagene-ai/), [Indica Labs](https://onco.cc/companies/indica-labs/), [Lunit](https://onco.cc/companies/lunit/), [Nucleai](https://onco.cc/companies/nucleai/), [Owkin](https://onco.cc/companies/owkin/), [Paige AI](https://onco.cc/companies/paige/), [PathAI](https://onco.cc/companies/pathai/), [Perimeter Medical Imaging AI](https://onco.cc/companies/perimeter-medical-imaging-ai/), [Proscia](https://onco.cc/companies/proscia/), [Strand AI](https://onco.cc/companies/strand-ai/), [Valar Labs](https://onco.cc/companies/valar-labs/), [X-Zell](https://onco.cc/companies/x-zell/)
- journals: [Analytical cellular pathology (Amsterdam)](https://onco.cc/journals/analytical-cellular-pathology/)
- technologies: [AI compute and model platforms for oncology](https://onco.cc/technologies/ai-compute-platforms/), [AI scoring of biomarkers and grade on pathology slides](https://onco.cc/technologies/ai-pathology-scoring/), [Multidisciplinary tumour boards](https://onco.cc/technologies/multidisciplinary-tumour-board/), [Multiplex immunofluorescence](https://onco.cc/technologies/multiplex-immunofluorescence/), [Pathology & radiology foundation models](https://onco.cc/technologies/pathology-foundation-model/), [Spatial transcriptomics](https://onco.cc/technologies/spatial-transcriptomics/), [Spatial-omics-guided treatment selection](https://onco.cc/technologies/spatial-omics-guided-therapy/), [Virchow / Virchow2 (Paige, MSK)](https://onco.cc/technologies/virchow/), [Whole-slide scanners and image management](https://onco.cc/technologies/whole-slide-scanners/)
- cancers: [Breast cancer (all types)](https://onco.cc/cancers/breast-cancer/), [HR-positive / HER2-negative breast cancer](https://onco.cc/cancers/breast-hr-positive/), [Localised prostate cancer, intermediate risk](https://onco.cc/cancers/prostate-intermediate-risk/), [Prostate cancer](https://onco.cc/cancers/prostate/), [Triple-negative breast cancer (TNBC)](https://onco.cc/cancers/tnbc/)
- terms: [Cancer AI vocabulary (CanSim terms map)](https://onco.cc/terms/cancer-ai-vocabulary/), [Cribriform growth pattern in prostate cancer](https://onco.cc/terms/cribriform-prostate-cancer/), [Digital pathology and whole-slide images (WSI)](https://onco.cc/terms/digital-pathology-wsi/), [Gleason score / Grade Group](https://onco.cc/terms/gleason-grade-group/), [H&E staining (haematoxylin and eosin)](https://onco.cc/terms/h-and-e-staining/), [Histology](https://onco.cc/terms/histology/), [Immunohistochemistry (IHC)](https://onco.cc/terms/ihc/), [Nottingham grade (breast cancer grade 1, 2 and 3)](https://onco.cc/terms/nottingham-grade/), [Oncology workforce](https://onco.cc/terms/oncology-workforce/), [Tumour-infiltrating lymphocytes (TILs)](https://onco.cc/terms/tils/)
- key papers: [American Society of Clinical Oncology/College Of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer](https://onco.cc/key-papers/paper-asco-cap-er-pr-testing-guideline-jco-2010/), [Clinical, pathological, and PAM50 gene expression features of HER2-low breast cancer](https://onco.cc/key-papers/paper-schettini-her2-low-features-npj-breast-cancer-2021/), [Spatially distinct tumor immune microenvironments stratify triple-negative breast cancers](https://onco.cc/key-papers/paper-gruosso-tnbc-spatial-immune-microenvironments-jci-2019/), [Tumor-Infiltrating Lymphocytes in Triple-Negative Breast Cancer](https://onco.cc/key-papers/paper-leon-ferre-tils-tnbc-no-chemotherapy-jama-2024/)
- ideas: [A dedicated fund for randomised trials of cancer AI with patient outcomes](https://onco.cc/ideas/idea-data-prospective-ai-trials-fund/), [A federated learning consortium of cancer centres that jointly own the models](https://onco.cc/ideas/idea-fund-federated-learning-consortium/), [A fund for prospective validation of academic biomarkers and companion diagnostics](https://onco.cc/ideas/idea-fund-biomarker-validation-fund/), [A pre-competitive consortium to train a shared multimodal cancer foundation model](https://onco.cc/ideas/idea-data-precompetitive-cancer-foundation-model/), [A registry of external validation datasets for cancer AI models, with mandatory reporting](https://onco.cc/ideas/idea-tr2-ai-external-validation-registry/), [A same-week expert second opinion for every rare cancer diagnosis](https://onco.cc/ideas/idea-bio2-rare-cancer-telepathology-network/), [A standard evaluation pathway for AI-assisted pathology, from reader study to deployment](https://onco.cc/ideas/idea-data-ai-pathology-evaluation-standard/), [A video-based surgical quality registry linking assessed skill to cancer outcomes](https://onco.cc/ideas/idea-fund-surgical-video-registry/), [AI quantification of HER2-low and HER2-ultralow](https://onco.cc/ideas/idea-ai-her2-low-scoring/), [AI second reads to stop borderline lesions being upgraded to cancer](https://onco.cc/ideas/idea-prev-pathology-ai-borderline-anchor/), [AI-first reading for high-volume common cancer diagnoses, pathologist for the exceptions](https://onco.cc/ideas/idea-acc-ai-first-pathology-common-cases/), [Calibrated reference slides so every lab scores HER2-low the same way](https://onco.cc/ideas/idea-tr2-her2-low-reference-materials/), [Clear the suppressive neutrophils out of pancreatic tumours first](https://onco.cc/ideas/idea-bio2-cxcr2-neutrophil-blockade/), [Continuous prospective validation for every oncology AI tool after deployment](https://onco.cc/ideas/idea-moon-continuous-ai-validation-registry/), [Digitise the nation's pathology slides and link them to outcomes](https://onco.cc/ideas/idea-data-national-slide-archive/), [Federated training of pathology and radiology models across hospitals](https://onco.cc/ideas/idea-data-federated-learning-imaging/), [Give exceptional responders less: pembrolizumab omission after complete response, anthracycline-free regimens and chemotherapy omission in lymphocyte-rich stage I disease](https://onco.cc/ideas/idea-tnbc-de-escalation-for-exceptional-responders/), [Harmonise PD-L1 testing for triple-negative breast cancer around one scored assay, with external quality assurance](https://onco.cc/ideas/idea-tnbc-pd-l1-assay-harmonisation/), [Map which tumour clones sit next to which immune cells before choosing therapy](https://onco.cc/ideas/idea-bio1-spatial-clone-immune-map/), [Match therapy to the type of scar-forming cell in the tumour](https://onco.cc/ideas/idea-bio2-caf-subtype-assignment/), [Mobile diagnostic units that biopsy, scan and treat on the same visit](https://onco.cc/ideas/idea-acc-mobile-see-and-treat-units/), [One certified open-source de-identification pipeline for scans and slides](https://onco.cc/ideas/idea-data-open-deidentification-pipeline/), [One digital PD-L1 scale that maps across all the competing assays](https://onco.cc/ideas/idea-tr2-pdl1-digital-calibration/), [Pathologist assistants plus AI triage to multiply pathologist capacity](https://onco.cc/ideas/idea-acc-pathologist-assistants-and-ai-triage/), [Patient-level multimodal foundation models for treatment selection](https://onco.cc/ideas/idea-multimodal-foundation-model/), [Public gold-standard datasets for validating every cancer biomarker test](https://onco.cc/ideas/idea-tr2-open-cdx-validation-sets/), [Real-time margin assessment and image-guided surgery as the global standard](https://onco.cc/ideas/idea-moon-image-guided-surgery-everywhere/), [Reflex re-scoring of HER2 0 versus 1+ with digital assistance so every eligible triple-negative patient reaches trastuzumab deruxtecan](https://onco.cc/ideas/idea-tnbc-her2-ultralow-testing-uptake/), [Sequestered, prospectively collected benchmark datasets that no one can train on](https://onco.cc/ideas/idea-data-sequestered-prospective-benchmarks/), [Slide scanners in district hospitals wired to pathologists anywhere](https://onco.cc/ideas/idea-acc-telepathology-district-network/), [Standards for spatial and multiplex tissue biomarkers before they reach the clinic](https://onco.cc/ideas/idea-tr2-spatial-biomarker-standards/), [The pathology lab triggers a trial referral the day a rare cancer is diagnosed](https://onco.cc/ideas/idea-tr1-pathology-triggered-referral/), [TIL-based omission of chemotherapy in stage I TNBC](https://onco.cc/ideas/idea-til-guided-deescalation/), [Turn the map of immune cells inside a tumour into a standardised test](https://onco.cc/ideas/idea-bio2-spatial-signature-cdx/), [Version control and locked reference sets for AI algorithms used as companion diagnostics](https://onco.cc/ideas/idea-tr2-ai-cdx-change-control/), [Whole-patient digital twins validated in prospective randomised trials](https://onco.cc/ideas/idea-moon-validated-digital-twins/)
- institutions: [Butaro Cancer Center of Excellence](https://onco.cc/institutions/butaro-cancer-center/), [IRCCS Regina Elena National Cancer Institute](https://onco.cc/institutions/regina-elena-rome/), [Laura and Isaac Perlmutter Cancer Center at NYU Langone Health](https://onco.cc/institutions/nyu-perlmutter/), [Medicines and Healthcare products Regulatory Agency](https://onco.cc/institutions/mhra/), [Memorial Sloan Kettering Cancer Center](https://onco.cc/institutions/mskcc/), [NCT/UCC Dresden, University Hospital Carl Gustav Carus](https://onco.cc/institutions/nct-dresden/)
- people: [Daniel E. Spratt](https://onco.cc/people/daniel-spratt/), [Faisal Mahmood](https://onco.cc/people/faisal-mahmood/), [Jakob Nikolas Kather](https://onco.cc/people/jakob-nikolas-kather/), [Thomas J. Fuchs](https://onco.cc/people/thomas-fuchs/)
- bottlenecks: [AI that is built but not validated or deployed](https://onco.cc/bottlenecks/b-ai-validation/), [Biomarkers are not validated or standardised](https://onco.cc/bottlenecks/b-biomarker-validation/), [Data silos](https://onco.cc/bottlenecks/b-data-silos/), [Most of the world has almost no cancer care](https://onco.cc/bottlenecks/b-global-access/), [No one can predict who responds to immunotherapy](https://onco.cc/bottlenecks/b-immunotherapy-response/), [Not enough oncologists, nurses, pathologists, physicists](https://onco.cc/bottlenecks/b-workforce/)

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