ReviewJournal of experimental & clinical cancer research : CR2025
The role of artificial intelligence-based foundation models and "copilots" in cancer pathology: potential and challenges.
Review in Journal of experimental & clinical cancer research : CR, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- The mycobiome, virome and archaeome in gastrointestinal cancers: molecular pathogenesis and therapeutic intervention.Molecular cancer · 2026Review
- Rapid slide-level assessment of glomerular yield using virtual staining of unstained renal biopsy sections.Diagnostic pathology · 2026Article
- Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.Histochemistry and cell biology · 2026Review
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Perineural invasion in solid tumors: biological foundations and the emerging integration of machine learning and artificial intelligence.Frontiers in oncology · 2026Review
- Digital pathology and artificial intelligence in breast and gynecologic oncology: from molecular prediction to multimodal integration.Frontiers in oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The integration of Artificial Intelligence (AI) into cancer pathology offers an imperative solution to global pathologist shortages and increasingly complex diagnostic demands. This review summarized the rapid evolution of AI in the field, highlighting the paradigm shift from task-specific (TS) algorithms towards powerful, versatile foundation models (FMs), such as UNI, CONCH, GigaPath, mSTAR, and Atlas. These models, trained on massive and diverse datasets using self-supervised and multimodal learning, demonstrate remarkable capabilities in cancer classification, subtyping, outcome prediction, and biomarker discovery. The emergence of AI "copilots", such as PathChat, SmartPath, further promises to streamline workflows through conversational interfaces and autonomous task planning. However, significant challenges impede clinical translation, including a validation crisis underscored by poor generalizability in zero-shot testing, critical concerns regarding model explainability ("black-box" nature), risks of hallucinations in generative tools, and ensuring generalizability and fairness across diverse populations. Robust external validation, standardized benchmarking, development of explainable AI approaches, and novel regulatory frameworks are essential to responsibly harness the transformative potential of foundation models and realize their promise in improving diagnostic accuracy, efficiency, and patient outcomes in cancer pathology.
Indexed as
Identifiers
What Socratic 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 Socratic graph.