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PocketHealth Launches AI-Powered Image Reader to Enhance Patient Understanding of Medical Imaging

by Fred Pennic 02/26/2025 Leave a Comment

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What You Should Know: 

– PocketHealth, a connected care company launches Image Reader, a new feature that uses AI to help patients better understand their medical imaging results. 

– Image Reader provides visual context within CT and X-ray scans, making it easier for patients to interpret their images and have more informed conversations with their healthcare providers.

AI-Powered Anatomical Identification

PocketHealth’s Image Reader leverages advanced AI, including the MedSAM (Medical Segmentation Anywhere Model) developed by AI scientist Dr. Bo Wang. This model is designed for universal medical image segmentation, enabling accurate identification of anatomical structures across various modalities.

The Image Reader automatically detects and labels organs and bones within medical images, providing patients with a clear and interactive understanding of their scans. This feature is currently optimized for a variety of CT and X-ray exams, with plans to expand support for additional modalities in the future.

Expanding PocketHealth’s Patient-Centered Toolkit

Image Reader complements PocketHealth’s existing suite of patient engagement tools, including:

  • Report Reader: Simplifies complex radiology reports with plain language explanations.
  • MyCare Navigator: Helps patients navigate their healthcare journey with personalized guidance and support.

“Medical imaging AI has primarily focused on clinical applications, but there’s an equally important opportunity to improve patient understanding,” said Dr. Bo Wang, Chief AI Scientist at the University Health Network (UHN), who led the development of MedSAM. “PocketHealth has taken an innovative approach by refining our segmentation  model  for real-world patient use. Using this technology to directly benefit patients is a meaningful step toward making medical imaging more accessible and insightful.”

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Tagged With: Artificial Intelligence, Medical Imaging Cloud, radiology

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