AI at Scale for Clinical Impact (ASCI): Cancer Pathology Foundation Models

AI for oncology is entering a new phase in which the central challenge is no longer simply training large models on digitized pathology slides, but building clinically reliable systems that learn from the full complexity of hospital-scale cancer data. Pathology remains the diagnostic cornerstone of oncology, yet modern cancer care increasingly depends on an interconnected multimodal ecosystem spanning whole-slide histology, immunohistochemistry, special stains, molecular assays, spatial and multiplexed imaging, genomics, clinical text, longitudinal records, and treatment outcomes. The AI at Scale for Clinical Impact (ASCI) workshop will bring together machine learning researchers, computer vision scientists, computational biologists, pathologists, oncologists, clinical informaticians, and industry leaders to define the next generation of scalable AI methods for cancer diagnosis, prognosis, therapy selection, and real-world clinical deployment. Building on the rapid emergence of pathology foundation models and the NeurIPS 2025 Self-supervised Learning for Cancer Pathology Foundation Models competition, this workshop expands the agenda beyond slide-centric representation learning toward multimodal, continually improving, clinically grounded AI systems. Core themes include pathology-aware and biology-aware learning, continual learning from growing institutional archives, data-efficient adaptation for rare cancers and emerging biomarkers, integration of gigapixel images with omics and clinical text, rigorous benchmarking and reporting standards, uncertainty estimation, interpretability, biological discovery, workflow integration, regulatory readiness, and prospective validation. By focusing on the full lifecycle from model development to measurable patient impact, ASCI aims to catalyze a cross-disciplinary research community around a central question: how can AI systems continuously learn from millions of patients and diverse cancer data streams while remaining trustworthy, generalizable, interpretable, and useful in real clinical practice?