HOPPR and AWS Introduce ‘Grace’, A Generative AI Model to Boost Medical Imaging Capabilities

US-based AI startup HOPPR The launch of Grace—a versatile foundation model tailored for medical imaging—aims to help clinicians and their support teams navigate diagnostic studies more effectively. Built using Amazon Web Services' (AWS) SageMaker, this model is currently available in private beta for developers, radiology vendors, and AI enterprises.

As per the official announcement, this access allows for model fine-tuning and development, representing a significant step forward in the company's goal of unlocking AI's vast potential. generative AI in medical imaging.

Dr. Khan M. Siddiqui, CEO of HOPPR, expressed enthusiasm about launching the beta version of their foundational model to trusted PACS vendors and developers. This initiative is designed to refine models and integrate feedback as they gear up for commercial rollout in the first quarter of 2024. He emphasized that Grace heralds a groundbreaking milestone for both HOPPR and the wider field of medical imaging, which stands to gain immensely from AI's capabilities to enhance healthcare quality and operational efficiency.

According to HOPPR, Grace serves as a B2B foundation model that transcends individual imaging types. It supports both image-to-image and text-to-image learning processes across various formats such as X-rays, CT scans, MRIs, and echocardiograms. medical imaging Accessible through an API service, Grace allows application developers to create AI-driven solutions that foster interactive engagement with medical imaging data.

With Grace's unique functionalities, users can derive critical diagnostic, clinical, and operational insights from medical imaging datasets. Its applications range from analyzing findings and proposing alternative imaging perspectives to assisting with surgical procedures and treatment plans.

In addition to clinical purposes, Grace is designed to accommodate non-clinical applications as well, including workflow improvements, billing and coding reviews, and quality control measures.

Privacy has been a priority throughout the development process; Grace has been built using over a petabyte of consented, anonymized medical imaging data. Moreover, it incorporates relevant reports and has undergone comprehensive training for robust commercial application across a wide array of datasets involving both 2D and 3D imaging.

The company has assured that this development complies with privacy-focused standards, aligning with the quality management systems integral to the healthcare industry.

According to the company, the foundation model HOPPR has noted that partnerships with entities like Gradient Health will ensure that Grace covers extensive longitudinal imaging studies, distinguishing it from typical generative models. It is projected that Grace will scale to approximately five trillion parameters, surpassing existing commercial models in complexity.

To fine-tune and align the model's offerings with the fast-evolving demands of the market, HOPPR is collaborating with industry frontrunners such as RadNet and Rad AI.

The company plans to conduct live demonstrations of Grace during the Radiological Society of North America (RSNA) annual conference, taking place from November 26 to 29, showcasing the model's potential applications within the radiology and healthcare sectors.

Looking ahead, HOPPR intends to leverage additional AWS resources, such as Health Imaging and Bedrock, to enhance data management, inferencing capabilities, and model development. The objective is to tackle significant barriers currently hindering optimal AI adoption in medical imaging, such as improving integration, expediting application development, and enhancing image resolution. healthcare sector As the healthcare domain prepares for technological advancements, Grace’s influence is expected to extend widely across clinical practice, workflow enhancements, and the overarching field of medical imaging innovation.

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