This thesis presents the development and evaluation of an organ-specific interactive AI algorithm designed for the segmentation of clinical CT datasets, specifically focusing on lung CT scans for demonstration purposes. The work addresses the challenge of automating medical image segmentation, a process traditionally performed manually by experts, which is both time-consuming and prone to variability. To overcome these limitations, the thesis proposes a semi-automatic approach using a 2D U-Net model that incorporates user interactions though the second channel of the CT image input. The model allows users to refine automatic segmentations by providing feedback through simple mouse clicks, iteratively improving the segmentation results.