ReviewJMA journal2025
Pathology Foundation Models.
Review in JMA journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- Artificial intelligence-assisted histopathological diagnosis of endocervical gastric-type adenocarcinoma: a multicenter model development and validation study.The journal of pathology. Clinical research · 2026Article
- Standardized, repeatable ulcerative colitis histology scoring and endpoint assessment using an automated, foundation model-based tool.medRxiv : the preprint server for health sciences · 2026Article
- FigATree: a novel framework for histological subtyping and grading of lung adenocarcinoma.Virchows Archiv : an international journal of pathology · 2026Article
- Pathology Foundation Models: Evolution, Current Landscape, Challenges and Opportunities from a Technical and Clinical Perspective.Bioengineering (Basel, Switzerland) · 2026Review
- GloPath: An Entity-Centric Foundation Model for Glomerular Lesion Assessment and Clinicopathological Insights.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial intelligence in genitourinary pathology.Histopathology · 2026Review
- Foundation Models in Cancer Pathology: Techniques, Applications, and Future Directions.Research (Washington, D.C.) · 2026Review
- AI-Powered Histology for Molecular Profiling in Brain Tumors: Toward Smart Diagnostics from Tissue.Cancers · 2025Review
- Computational pathology in breast cancer: optimizing molecular prediction through task-oriented AI models.NPJ breast cancer · 2025Review
- The role of artificial intelligence-based foundation models and "copilots" in cancer pathology: potential and challenges.Journal of experimental & clinical cancer research : CR · 2025Review
- Multi-Modal Foundation Models for Computational Pathology: A Survey.Transactions on machine learning research · 2025Article
- The Rise of AI-Assisted Diagnosis: Will Pathologists Be Partners or Bystanders?Diagnostics (Basel, Switzerland) · 2025Review
- Transformer-BasedDiagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence-driven pathomics in hepatocellular carcinoma: current developments, challenges and perspectives.Discover oncology · 2025Review
- MiroSCOPE: An AI-driven digital pathology platform for annotating functional tissue units.bioRxiv : the preprint server for biology · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pathology plays a crucial role in diagnosing and evaluating patient tissue samples obtained via surgeries and biopsies. The advent of whole slide scanners and the development of deep learning technologies have considerably advanced this field, promoting extensive research and development in pathology artificial intelligence (AI). These advancements have contributed to reduced workload of pathologists and supported decision-making in treatment plans. Large-scale AI models, known as foundation models (FMs), are more accurate and applicable to various tasks than traditional AI. Such models have recently emerged and expanded their application scope in healthcare. Numerous FMs have been developed in pathology, with reported applications in various tasks, such as disease and rare cancer diagnoses, patient survival prognosis prediction, biomarker expression prediction, and scoring of the immunohistochemical expression intensity. However, several challenges persist in the clinical application of FMs, which healthcare professionals, as users, must be aware of. Research to address these challenges is ongoing. In the future, the development of generalist medical AI, which integrates pathology FMs with FMs from other medical domains, is expected to progress, effectively utilizing AI in real clinical settings to promote precision and personalized medicine.
Indexed as
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.