RedBrick AI Raises US$4.6M in Funding to Accelerate the Development of Healthcare AI
Health-tech AI platform RedBrick AI is today announcing a US$4.6M seed funding round to accelerate the development and adoption of artificial intelligence in clinical settings, through rapid data annotation on medical imagery. The round was led by Surge, Sequoia Capital India, and Southeast Asia’s rapid scale-up program, with participation from Y Combinator and angels.
Medical imagery is an essential source of truth in clinical diagnosis and comprises about 90% of all healthcare data. AI systems can dramatically shorten the time to diagnosis, improve clinician productivity by triaging high-importance cases, and act as the first line of defense in under-staffed clinical environments. Researchers and healthcare institutions are increasingly investing in AI solutions to improve diagnostics, treatment and patient quality of care. The use of AI in healthcare stands to transform patient care by boosting clinician productivity and automating clinical diagnosis. In 2021 alone, the U.S. Food and Drug Administration (FDA) approved 115 AI algorithms for use in medical environments, an 83% increase from 2018. However, researchers cannot use medical images to train AI systems until they are cleaned and expertly annotated. Training an AI system also requires hundreds of annotated medical images and thousands of hours of annotation by clinicians. Due to the complexity, size, and unique nature of medical images, clinicians have to resort to traditional and difficult-to-use clinical tools to perform annotations. RedBrick AI is thus laser-focused on solving the first key challenge to healthcare AI adoption – providing clinicians with high-quality data annotation tools that accelerate the preparation of training datasets.
RedBrick AI CEO and co-founder Shivam Sharma commented: “Working with leading healthcare AI teams over the past year has been an incredible journey and learning opportunity. With the rapid growth of artificial intelligence in clinical settings, researchers need excellent tools to build high-quality datasets and models at scale. Our customers are in the vanguard of this growth, pioneering everything from surgical robots to automated detection of cancers. We are incredibly excited to use the funds we’ve raised to power the next generation of researchers in building AI for clinical settings.”
RedBrick AI’s tools address several challenges unique to medical data annotation, such as the complexity of existing annotation tools, quality control and machine learning integration. The platform’s specialized annotation tools can be accessed through the browser and are designed to be used without prior training. RedBrick AI also offers semi-automated tools to annotate complex 3D medical images.
RedBrick has a robust quality control process to ensure the quality of annotations, which are crucial to securing AI algorithm certifications from regulators. It involves efficient quality control workflows on the platform that can compile the opinions of several clinicians per annotation case, all while dramatically reducing time spent on project management. Its API also helps machine learning engineers integrate with their cloud and clinical data stores, for example, AWS or hospital enterprise PACS servers. The APIs are used to build ML data pipelines.