ArticleNPJ digital medicine2025
PathOrchestra: a comprehensive foundation model for computational pathology with over 100 diverse clinical-grade tasks.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- A universal visual foundation model for computational cytopathology.Nature cancer · 2026Article
- A straightforward framework to harmonize computational pathology.Journal of pathology informatics · 2026Article
- Targeting IFN-I driven tertiary lymphoid structures halts B cell-mediated bone loss in periodontitis.International journal of oral science · 2026Article
- Application and exploration of digital pathology technology in standardized residency training.BMC medical education · 2026Article
- Radiomics: Current Applications and Future Directions.MedComm · 2026Review
- The Augmented Cytopathologist: A Conceptual Exploratory Narrative Review on Immersive and Vision-Language Models Tools in Digital Pathology.Journal of imaging · 2026Review
- Foundation models in computational pathology: methods, applications and clinical implications.BMJ oncology · 2026Review
- Foundation models: Insights and implications for gastrointestinal cancer.World journal of gastroenterology · 2025Review
- Comprehensive Benchmark Dataset for Pathological Lymph Node Metastasis in Breast Cancer Sections.Scientific data · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
30 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The complexity and variability of high-resolution pathological images present significant challenges in computational pathology. While AI-driven pathology foundation models have advanced the field, they require large-scale datasets, substantial storage, and significant computational resources, as well as rigorous validation for clinical applicability. We present PathOrchestra, a versatile pathology foundation model trained on 287,424 slides from 21 tissue types across three centers. Evaluated on 112 tasks from 61 private and 51 public datasets, covering digital slide preprocessing, pan-cancer classification, lesion identification, multi-cancer subtype classification, biomarker assessment, gene expression prediction, and structured report generation. Across 27,755 whole slide images and 9,415,729 region-of-interest images, it achieved over 0.950 accuracy in 47 tasks, including pan-cancer classification, lymphoma subtyping, and bladder cancer screening. It is the first to generate structured reports for colorectal cancer and lymphoma. Overall, PathOrchestra demonstrates the clinical readiness of large-scale self-supervised pathology foundation models, achieving high accuracy and offering potential to digital medicine integration.
Identifiers
What OpenQuestion holds
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.