Routine pathology slides reveal how a tumor looks—but could they also offer clues to its chromosomal instability (CIN) ? CIN is relevant to breast cancer prognosis and treatment stratification, yet laboratory-based assessment can be time-consuming and costly. Predicting CIN from H&E-stained WSIs requires AI models to connect local cellular patterns with the surrounding tissue architecture.

Liquid biopsy offers a minimally invasive approach to detecting tumor-derived biomarkers, yet its clinical translation remains constrained by low biomarker abundance and complex biological matrices. As AI advances, a key question is how computational models can integrate with analytical chemistry platforms to support precision oncology.

Medical imaging demands more than strong reasoning: subtle visual patterns, precise measurements, and changes across successive scans can all shape clinical decisions. As multimodal AI advances, a key question is how general-purpose models can work with specialized imaging tools to support oncology care.