Researchers are exploring the use of large models and task-driven frameworks in digital pathology, with a focus on applications such as breast cancer and gastrointestinal pathology. Various approaches are being developed, including the use of multimodal large language models, neural architecture search, and adaptive diagnostic reasoning frameworks. These methods aim to improve disease diagnosis and molecular prediction in digital pathology, with some studies focusing on specific subspecialties or types of cancer.