This presentation will highlight the critical role of high-quality medical imaging metadata in optimizing AI algorithms. It will emphasize the importance of data quality in AI-powered healthcare, showcasing how consistent, accurate, and comprehensive metadata enhances algorithm accuracy, reliability, and efficiency.
This presentation will explore essential steps for ensuring data quality, encompassing normalization, standardization, and adhering to industry standards like DICOM and IHE. It also emphasizes the integration of data quality best practices into the imaging workflow, ultimately promoting the effective utilization of metadata to optimize AI performance.
Objectives
- Understand the Impact of Metadata Quality on AI Algorithms: Participants will learn how the quality of medical imaging metadata directly affects the accuracy, reliability, and efficiency of AI algorithms in healthcare settings.
- Identify Steps to Enhance Data Quality: Attendees will explore essential techniques such as normalization, standardization, and adherence to industry standards (DICOM, IHE) necessary for ensuring high-quality medical imaging metadata.
- Integrate Data Quality Practices into Imaging Workflows: Participants will gain insights into how to incorporate data quality best practices into medical imaging workflows to optimize the performance of AI technologies in healthcare.
Resources
CE Credit
SIIM has approved this activity for 1.0 hours of SIIM IIP Credits towards certification and re-certification by the American Board of Imaging Informatics (ABII).
To receive credit, registrants must view the entire webinar and then complete the post-webinar survey. Webinar credits will only be awarded one time per webinar view, regardless of if the learner watches the content live or on-demand.
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To access the webinar, once you are registered navigate to My Learning in your My SIIM Account profile.
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Chelsi Jones, CIIP
John Sole, MBA
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