What’s new in digital and computational pathology 2026: advances in adoption, standards, AI technologies, and clinical integration

Digital and computational pathology are expanding rapidly worldwide, driven by advances in whole-slide imaging, AI algorithms, multimodal data integration, and improved digital infrastructure. Adoption continues to accelerate in the United States and internationally, supported by professional guidelines, emerging reimbursement pathways, and the growing need for remote workflows and collaborative diagnostics. Progress in interoperability standards, regulatory frameworks, and FDA approvals has strengthened the foundation for clinical deployment, while large-scale data repositories and federated learning approaches enable more robust and privacy-preserving model development. Foundation models, multimodal AI systems, and LLM-based copilots are reshaping diagnostic support, prognostication, workflow efficiency, clinical trials and drug discovery.

Authors

Publication Details

Journal
Journal of Pathology and Translational Medicine
Published
2026-05-15
DOI
https://doi.org/10.4132/jptm.2026.04.27
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
0.00
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article

What’s new in digital and computational pathology 2026: advances in adoption, standards, AI technologies, and clinical integration

Chadi Hajar, Selim Sevim, Snehal Sonawane
Journal of Pathology and Translational Medicine
AI in cancer detection
article

What’s new in digital and computational pathology 2026: advances in adoption, standards, AI technologies, and clinical integration

Chadi Hajar, Selim Sevim, Snehal Sonawane
article en

Abstract

Digital and computational pathology are expanding rapidly worldwide, driven by advances in whole-slide imaging, AI algorithms, multimodal data integration, and improved digital infrastructure. Adoption continues to accelerate in the United States and internationally, supported by professional guidelines, emerging reimbursement pathways, and the growing need for remote workflows and collaborative diagnostics. Progress in interoperability standards, regulatory frameworks, and FDA approvals has strengthened the foundation for clinical deployment, while large-scale data repositories and federated learning approaches enable more robust and privacy-preserving model development. Foundation models, multimodal AI systems, and LLM-based copilots are reshaping diagnostic support, prognostication, workflow efficiency, clinical trials and drug discovery.

Journal of Pathology and Translational MedicineVol. 60(3)
Industry, innovation and infrastructure
Openalex Percentile: Top 3%
AI in cancer detection
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