E-submission
Search
- Page Path
- HOME > Search
Newsletter
- What’s new in digital and computational pathology 2026: advances in adoption, standards, AI technologies, and clinical integration
- Selim Sevim, Chadi Hajar, Snehal Sonawane
- J Pathol Transl Med. 2026;60(3):364-370. Published online May 15, 2026
- DOI: https://doi.org/10.4132/jptm.2026.04.27
- 10,823 View
- 274 Download
- 2 Crossref
-
Abstract
PDF - 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.
-
Citations
Citations to this article as recorded by
- FTU-Seek: Foundation Model-Guided Hard-Negative Learning for Sparse Functional Tissue Unit Segmentation
Zonghao Liu, Lei Su, Jiguang Yu, Xuqing Geng, Louis Shuo Wang, Jianmin Wang, Jingfeng Liu
Biomedicines.2026; 14(9): 1935. CrossRef - Lightweight deep learning models for histopathological image analysis in resource-constrained healthcare environments
Ahmed Adedeji Adeniyi, Steve Adetunji Adeshina, Yusuf Abass Aleshinloye, Roseline Oluwaseun Ogundokun, Pius Adewale Owolawi
Intelligence-Based Medicine.2026; 15: 100475. CrossRef
- FTU-Seek: Foundation Model-Guided Hard-Negative Learning for Sparse Functional Tissue Unit Segmentation
First
Prev
