Hyun-Jong Jang, Sung Hak Lee
Pathology is fundamental to precision oncology, offering molecular and morphologic insights that enable personalized diagnosis and treatment. Recently, deep learning has demonstrated substantial potential in digital pathology, effectively addressing a wide range of diagnostic, prognostic, and biomarker-prediction tasks. Although early approaches based on convolutional neural networks had limited capacity to generalize across tasks and datasets, transformer-based foundation models have substantially advanced the field by enabling scalable representation learning, enhancing cross-cohort robustness, and supporting few- and even zero-shot inference across a wide range of pathology applications. Furthermore, the ability of foundation models to integrate heterogeneous data within a unified processing framework broadens the possibility of developing more generalizable models for medicine. These multimodal foundation models can accelerate the advancement of pathology-based precision oncology by enabling coherent interpretation of histopathology together with radiology, clinical text, and molecular data, thereby supporting more accurate diagnosis, prognostication, and therapeutic decision-making. In this review, we provide a concise overview of these advances and examine how foundation models are driving the ongoing evolution of pathology-based precision oncology.