We have designed a simple, user-friendly interface that gives clinicians accurate and actionable insights to support better patient care. Some interstitial lung abnormalities (ILAs) are known to have a high risk of progression and mortality, so detecting them early is crucial. Our approach helps doctors plan treatments more effectively, giving patients a better chance at improved outcomes and survival. We added this feature to our chest CT analysis product, providing a more comprehensive overview that includes both major and incidental findings. Our AI-powered system automates the reporting process, cutting down the time needed for analysis while ensuring radiologists get a detailed, reliable report.

The abnormality types are estimated for the chest CT scans, at the time of this study. We are working on adding the location information for better interpretation so the doctors can locate each abnormality. Moreover, we added eight abnormality types for now. We will add more types for better diagnosis.

In future studies, we plan to expand our model to include additional abnormalities along with precise localization data, further enhancing its capability to support radiologists in early detection and diagnosis. This will not only improve the accuracy of AI-assisted reporting but also contribute to more efficient and reliable patient care.