From DICOM Limitations to AI Solutions Larry Sitka’s Insights | Unboxing AI Ep #14

In Episode #14 of Unboxing AI by CARPL.ai, Larry Sitka, EVP & CSIO Enterprise Imaging and AI at PaxeraHealth, explores the revolutionary impact of AI on radiology and healthcare. Drawing on his extensive experience, from leading informatics at Canon to founding Acuo Technologies, LLC, a pivotal company in VNA, Larry addresses the need for PACS and VNA systems to adapt to AI's demands for high data volume and continuous operation. He underscores the necessity of overhauling these systems to better integrate AI, ultimately enhancing diagnostic accuracy and efficiency.
Key Points Discussed:
▪️ Larry discussed the need to update PACS and VNA systems to handle AI’s large data requirements and continuous use.
▪️ He pointed out issues with DICOM standards, such as inconsistencies and challenging data migrations, and stressed the need for better data synchronization.
▪️ Larry suggested moving AI algorithms closer to the data to reduce transmission time and costs, which can improve accuracy and reduce false positives.
▪️ He advised AI developers to use standardized containerization, verify algorithms, and plan for future technology in their designs.
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