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 designed to simplify workflows while improving output quality.
The first tool enables radiologists to annotate original mammography images with high precision. It incorporates a structured validation workflow, where annotations created by one radiologist can be reviewed and verified by another. This dual-layer system ensures higher accuracy and reduces inter-observer variability. A critical part of this process involved designing intuitive user flows and wireframes in Figma before development—ensuring that the solution aligned closely with clinical usability expectations.
The second tool introduces a more advanced human-AI collaboration model. It displays AI-generated predictions alongside the original images, allowing radiologists to validate model outputs and identify missed abnormalities. This creates a continuous feedback loop for model fine-tuning. Here, my role extended beyond development into understanding stakeholder needs, prioritizing features, and ensuring that the solution delivered measurable value in terms of both efficiency and model performance.
A standout feature across both tools is the use of AI-generated bounding boxes. These significantly reduce the cognitive load on radiologists by pre-highlighting areas of interest. Instead of starting from scratch, users can focus on refining and validating predictions. This not only accelerates the annotation process but also improves consistency—demonstrating how thoughtful product design can directly impact user productivity.
Key Results
- Reduced annotation time significantly by streamlining workflows and introducing AI-assisted bounding boxes
- Collaborated with and incorporated feedback from multiple senior radiologists to improve usability and accuracy
- Improved annotation consistency and dataset quality, directly contributing to better model performance
From a business standpoint, the impact is clear. High-quality annotations lead to better-performing AI models, which in turn improve diagnostic accuracy and patient outcomes. Additionally, reducing annotation time lowers operational costs and speeds up AI development cycles. These are critical metrics that align both technical execution and business goals.
This experience has been pivotal in shaping my transition from a purely development-focused role to one that bridges technology, users, and business outcomes. By identifying a real-world problem, driving solution design—including early-stage wireframing—and delivering measurable impact, I have developed skills that align closely with an Associate Project Manager role: stakeholder management, problem-solving, prioritization, and outcome-driven execution.
As healthcare continues to embrace AI, the need for professionals who can connect technical innovation with real-world impact is growing. This journey reflects not just a product I built, but a mindset shift toward ownership, leadership, and delivering value at scale.
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