What You Should Know:

你应该知道的：

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PocketHealth

口袋健康

, a connected care company launches

，一家互联护理公司推出

Image Reader

图像阅读器

, a new feature that uses

，一个使用的新功能

AI

人工智能

to help patients better understand their medical imaging results.

帮助患者更好地理解他们的医学影像结果。

– 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.

– 图像阅读器在CT和X光扫描中提供视觉背景，使患者更容易解读自己的图像，并与医疗服务提供者进行更有根据的对话。

AI-Powered Anatomical Identification

人工智能驱动的解剖学识别

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.

PocketHealth的图像阅读器利用了先进的AI技术，包括由AI科学家王波博士开发的MedSAM（医学任意分割模型）。该模型专为通用医学图像分割设计，能够在各种模态下准确识别解剖结构。

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..

图像阅读器会自动检测并标记医学图像中的器官和骨骼，为患者提供对其扫描结果清晰且交互式的理解。该功能目前针对多种CT和X光检查进行了优化，并计划未来扩展对其他成像模式的支持。

Expanding PocketHealth’s Patient-Centered Toolkit

扩展PocketHealth以患者为中心的工具包

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

图像阅读器补充了PocketHealth现有的患者参与工具套件，包括：

Report Reader:

报告阅读器：

Simplifies complex radiology reports with plain language explanations.

简化复杂的放射学报告，用通俗易懂的语言解释。

MyCare Navigator:

我的护理导航员：

Helps patients navigate their healthcare journey with personalized guidance and support.

帮助患者通过个性化的指导和支持来管理他们的医疗旅程。

“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.

“医学影像人工智能主要集中在临床应用上，但同样重要的是提升患者对病情的理解，”多伦多大学健康网络（UHN）首席人工智能科学家、MedSAM开发负责人王波博士表示。“PocketHealth通过优化我们的分割模型以适用于真实世界的患者使用，采取了一种创新的方法。”

Using this technology to directly benefit patients is a meaningful step toward making medical imaging more accessible and insightful.”.

利用这项技术直接造福患者，是让医疗影像更加普及和富有洞察力的一个有意义的步骤。