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Pathology & radiology foundation models

Very large AI models trained on millions of slides or scans that can be adapted to almost any diagnostic question.

Virchow (Paige/MSK, 1.5M slides), UNI and CONCH (Harvard), Prov-GigaPath (Microsoft/Providence), PLUTO, and radiology models (Merlin, RadFM). They predict molecular alterations, prognosis, and treatment response from routine H&E and CT, and power the FDA-cleared ArteraAI tools. Multimodal patient-level models integrating genomics, imaging, and notes are in development (e.g., CanSim-style efforts, Tempus, Owkin).

Schematic · not to scale
Stained protein (HER2, PD-L1) · Glass slide · Whole-slide scan → model

How it works

Self-supervised pretraining (DINOv2, contrastive) on unlabelled images; frozen encoder plus small task heads.

Strengths
  • Data-efficient adaptation
  • Discover morphology-genotype links
Limitations
  • Validation across sites
  • Regulatory treatment of general-purpose models

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Literature trend152 papers in the last 12 months+334% vs prior 12How this is computed
Latest papers · live from Europe PMC
Open in Europe PMC

Query for this technology: (TITLE:"foundation model" OR ABSTRACT:"foundation model") AND (TITLE:"pathology" OR ABSTRACT:"pathology" OR TITLE:"histopathology" OR ABSTRACT:"histopathology" OR TITLE:"radiology" OR ABSTRACT:"radiology") AND (cancer OR tumor OR tumour OR oncology OR carcinoma OR lymphoma OR leukemia OR leukaemia OR myeloma OR sarcoma OR melanoma OR glioma). Results are unfiltered search hits about Pathology & radiology foundation models, not a curated reading list.

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