Accounting for past imaging studies: Enhancing radiology AI and reporting
The use of self-supervision from image-text pairs has been a key enabler in the development of scalable and flexible vision-language AI models in not only general domains but also in biomedical domains such as radiology. The goal in the radiology setting is to produce rich training signals without requiring manual labels so the models can learn to accurately recognize and locate findings in the images and relate them to content in radiology reports.
Radiologists use radiology reports to describe imaging findings and offer a clinical diagnosis or a range of possible diagnoses, all of which can be influenced by considering the findings on previous imaging studies. In fact, comparisons with previous images are crucial for radiologists to make informed decisions. These comparisons can provide valuable context for