When launching AI programs, A company’s clinical development team faced an urgent need for robust quality control processes, secure infrastructure, and efficient training data workflows to accelerate time-to-market for critical medical imaging models.
The company adopted Databrewery’s collaborative platform along with its secure and adaptable deployment capabilities. This enabled rapid implementation of workflows that supported expert-led labeling and strict data governance.
The company successfully scaled its training data pipeline by enabling clinical experts to guide annotation teams. As a result, labeled medical image data is now produced with significantly reduced cost, faster turnaround, and improved quality, achieving up to 10 times lower cost and five times higher speed.
The company, a long-standing leader in biotechnology and medical innovation, has expanded its focus into machine learning to support diagnostic advancements. Researchers are now building convolutional neural networks that assist medical professionals by analyzing clinical imagery to detect early signs of disease.
Unlike traditional algorithms that rely on idealized inputs, real-world patient data often includes irregularities. For instance, a healthy retinal image may be straightforward to interpret, but images from actual patients frequently include complications such as lesions or other clinical indicators. Training models to accurately identify and classify such images demands highly detailed and precise annotations across large volumes of medical data.
Due to the critical nature of clinical outcomes, annotation tasks for this type of data are typically performed only by qualified medical professionals. Ensuring annotation accuracy is essential, as even minor errors in training data can contribute to incorrect predictions and pose serious risks in medical decision-making. However, relying exclusively on medical experts for all labeling work significantly limits scalability due to time and cost constraints.
To address this challenge, the company implemented a labeling workflow using Databrewery that allowed subject matter experts to instruct and guide professional labelers in handling medical imagery. Annotators handled the majority of labeling tasks, while expert reviewers performed quality checks to maintain clinical reliability. This approach enabled the team to maintain high standards without compromising throughput.
Through this collaborative model, the company created an efficient pipeline for generating labeled data, drastically reducing both cost and turnaround time. By shifting from a fully expert-driven process to a guided review-based system, the company now produces labeled datasets for medical image analysis up to ten times more cost-effectively and five times faster, all while improving annotation quality and supporting the safe development of AI-enabled diagnostic.