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.

• Foundation models in pathology have plateaued in clinical performance because they're built on flawed assumptions that more data and scale automatically lead to better generalization
• Models struggle to generalize across different institutions and real-world clinical settings, undermining their core value proposition
• Weakly supervised learning, a task-specific alternative, has achieved high accuracy without needing the massive scale foundation models rely on
• The field needs to shift from chasing generalized, one-size-fits-all solutions toward targeted, task-specific approaches