Computational pathology in breast cancer: optimizing molecular prediction through task-oriented AI models

Abstract

The integration of artificial intelligence (AI) in breast cancer pathology has been driven by the promise of "big data"-based foundation models: large deep learning systems deriving diagnostic and prognostic insights from digitized whole slide images (WSIs). Yet, despite progress in computational power and architectures, these models face obvious barriers to clinical use, including poor workflow integration, limited explainability, and reduced generalizability across diverse clinical settings. This article examines the opportunities provided by small, task-oriented AI models designed to predict clinically relevant molecular features in breast cancer, such as hormone receptors (HRs), HER2, Ki-67, BRCA-related status, and somatic mutations directly from WSIs. To overcome foundation model constraints, approaches like model distillation, weak supervision, and modular training are critically examined. Progress now depends on high-quality datasets, rigorous multi-institutional validation, and collaboration between computational scientists, clinicians, and regulators to deliver explainable, clinically actionable innovations in breast cancer.

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