Foundation models in pathology are failing not due to size or training duration but because they are built on flawed assumptions about data scalability and generalization. Clinical performance has plateaued, as models struggle with variability across institutions and real-world applications, highlighting a need for task-specific approaches instead of generalized solutions. Alternative methods, like weakly supervised learning, have shown promise in achieving high accuracy without the limitations of foundation models.

The article discusses the Mahmood Lab's innovative use of the Dinov2 model to analyze human pathology, enhancing the accuracy and efficiency of medical imaging and diagnostics. By integrating advanced AI techniques, the lab aims to improve understanding and outcomes in various health conditions.

The article discusses the intricate details of cancer, highlighting how advancements in pathology and genetics have revealed significant variations in tumor types that correlate with disease behavior and treatment responses. It emphasizes the historical evolution of cancer understanding, particularly the role of genetic mutations and protein expression in developing targeted therapies like trastuzumab for HER2-positive breast cancer patients.