Wrangling medical imaging data: Strategies to streamline AI-powered workflows

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The value of artificial intelligence (AI) and machine learning (ML) in medical imaging is undeniable: more accurate diagnoses, predictive insights and streamlined workflows. However, as Big Pharma and medical research institutions amplify their AI and ML endeavors, they confront pivotal challenges. Chief among these challenges are the intricacies of labeling and annotating medical images.

Approximately 80% of the time it takes to prepare real-world data for downstream analysis is used on seemingly foundational tasks like locating, curating and structuring data. When we narrow our focus to medical imaging, the stakes rise significantly. The sheer volume of data required to do this work makes using human-only annotation close to impossible. And in this domain, the most minor details can critically influence diagnostic accuracy, making it imperative that data is not only accessible but clearly organiz…

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