DeepHealth, a subsidiary of RadNet, and a leader in AI-driven radiology and health informatics, has partnered with HOPPR (www.hoppr.ai) to advance AI technologies.

The collaboration will focus on developing a Medical-Grade Generalized Foundational Model and Fine-Tuned models for detecting breast, prostate, and lung cancers, leveraging generative AI and diverse data sets to enhance diagnostic capabilities.

HOPPR’s Medical-Grade Generalized Foundation Model is designed to support medical research and streamline data collection and training.

Foundational Models serve as adaptable, pre-trained systems that can be customized for specific tasks, essential for specialized diagnostic applications.

This partnership will enable DeepHealth to rapidly develop Fine-Tuned models, improving their AI-driven health informatics solutions and advancing radiology.

The integration of HOPPR’s models is expected to enhance diagnostic accuracy, expedite image analysis, and incorporate generative AI into non-clinical applications such as workflow automation. DeepHealth aims to integrate AI-based automation into radiology workflows comprehensively.

HOPPR’s advanced infrastructure for AI and machine learning, combined with DeepHealth’s clinical expertise and experience in deploying AI tools, is set to unlock significant value from medical imaging data and advance imaging technologies.

The collaboration is designed to address challenges across the radiology value chain, including referral management, scheduling, patient engagement, and workflow efficiency. DeepHealth OS offers a comprehensive solution for medical imaging, including operational tools and end-to-end services.

DeepHealth and RadNet’s digital health technologies are utilized in over 800 clinical sites globally, performing more than fifteen million exams annually and generating over two million AI-informed diagnoses.
 

Source: radnet.com

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