Journal of the National Cancer Institute
October 21, 2026 · via Europe PMC
Foundation models for cancer: advancing diagnosis, prognosis, and treatment response-an NCI workshop report.
Rizwan A, Makhlouf HR, Robles AI, +8 more, Sorg BS.
AI summary — for doctors, generated from the abstract only
An NCI workshop concluded that cancer AI foundation models need high-quality representative data, real-world prospective validation and attention to implementation and governance.
What was studied
Report of a March 2026 three-day NCI workshop with over thirty speakers on foundation models across single-cell biology, pathology, imaging, EHR, drug discovery, federated learning and decision support.
Key findings
- Robust models depend on multimodal datasets representative of real patients and settings.
- Prospective real-world evaluation is essential to establish reliability and generalisability.
- Translation also needs workflow integration, interpretability and data governance.
Clinical relevance
Oncologists should expect AI tools to arrive but should require evidence of validation in comparable populations.
Who should care
Limitations
A workshop summary reflecting expert opinion, without systematic evidence review or quantitative results.
Ask for prospective validation and clinical workflow fit before adopting any oncology AI tool.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
From March 24-26, 2026, the National Cancer Institute convened the Foundation Models for Cancer Workshop, bringing together more than thirty speakers from academia, government, and industry and attracting more than 2,000 registrants worldwide. The workshop examined the current state and future potential of foundation models and related artificial intelligence (AI) technologies in oncology. Presentations spanned single-cell biology, computational pathology, medical imaging, electronic health records, drug discovery, federated learning, and clinical decision support. Several themes emerged across the three-day meeting. First, the development of robust foundation models depends on access to high-quality, multimodal cancer datasets that are representative of the patient populations, disease stages, and clinical settings encountered in routine care. Second, prospective validation and evaluation in real-world clinical environments are essential for establishing the reliability, generalizability, and utility of AI-enabled tools. Third, while technical advances continue at a rapid pace, successful translation into cancer research and patient care will also require attention to implementation, workflow integration, interpretability, and data governance. Collectively, the workshop highlighted both the opportunities and challenges associated with applying foundation models to cancer research and clinical care and provided a forum for discussing future directions for the field.
DOI
10.1093/jnci/djag397
PMID
42905499
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
Want more like this?
Browse more from the feed. A free account unlocks the full feed, saving and communities.
Back to the research feed