Human-AI Collaboration in Mammography: Building Tools That Enhance Diagnostic Accuracy

In the rapidly evolving landscape of medical imaging, mammography remains a cornerstone for early breast cancer detection. However, beyond the algorithms and models, one of the most critical yet underappreciated challenges lies in the annotation of mammography images. Through my experience as a developer working closely with radiologists and AI systems, I identified this bottleneck and took ownership of building solutions that not only solve technical problems but also improve clinical workflows—marking my transition toward a more product and project-oriented role. Annotating mammograms is inherently complex. Radiologists must detect subtle abnormalities such as microcalcifications and faint masses, often under significant time pressure. Traditional tools are not optimized for this level of precision, leading to inefficiencies, inconsistencies, and delays in building high-quality datasets for AI training. Recognizing this gap, I led the development of two complementary annotation tools