Most data in cancer research is tabular: patient records, biomarker panels, clinical trial results, and features derived from omics. For decades, gradient-boosted trees have dominated this domain, and deep learning has struggled, especially in the small-sample regime typical of clinical studies. TabPFN is a tabular foundation model that changes this picture. Pre-trained on millions of synthetic datasets, it performs in-context learning: given a new dataset, it yields well-calibrated predictions in seconds, without hyperparameter tuning, and natively handles missing values, outliers, and mixed data types. In this talk, I will explain the ideas behind TabPFN, our Nature 2025 paper, and the fast-paced developments since then. TabPFN has already been used in hundreds of medical prediction use cases, and we are keen to work with partners to identify & solve the hardest challenges of tabular predictions whose solutions would bring the greatest benefit to society.
