  Pathology has played a crucial role in the diagnosis and evaluation of
patient tissue samples obtained from surgeries and biopsies for many years. The
advent of Whole Slide Scanners and the development of deep learning
technologies have significantly advanced the field, leading to extensive
research and development in pathology AI (Artificial Intelligence). These
advancements have contributed to reducing the workload of pathologists and
supporting decision-making in treatment plans. Recently, large-scale AI models
known as Foundation Models (FMs), which are more accurate and applicable to a
wide range of tasks compared to traditional AI, have emerged, and expanded
their application scope in the healthcare field. Numerous FMs have been
developed in pathology, and there are reported cases of their application in
various tasks, such as disease diagnosis, rare cancer diagnosis, patient
survival prognosis prediction, biomarker expression prediction, and the scoring
of immunohistochemical expression intensity. However, several challenges remain
for the clinical application of FMs, which healthcare professionals, as users,
must be aware of. Research is ongoing to address these challenges. In the
future, it is expected that the development of Generalist Medical AI, which
integrates pathology FMs with FMs from other medical domains, will progress,
leading to the effective utilization of AI in real clinical settings to promote
precision and personalized medicine.
