Pathology Specific

Computational pathology leverages artificial intelligence, particularly deep learning, to analyze histopathology images and associated textual reports, aiming to improve diagnostic accuracy and efficiency. Current research heavily focuses on vision-language models, including transformer-based architectures like BLIP-2 and custom-trained models such as PathologyBERT, to integrate image and text data for tasks like report generation, image retrieval, and zero-shot classification. These advancements hold significant promise for accelerating research, improving diagnostic workflows, and ultimately enhancing patient care by enabling more precise and timely diagnoses.