What You Should Know:
– HOPPR, a company developing artificial intelligence (AI) solutions for medical imaging, has announced the launch of Grace, a multimodal foundation model that enables image-to-image and text-to-image learning across all medical imaging modalities.
– Grace is available via private beta to developers, radiology PACS, and AI companies for fine-tuning and application development.
Key Benefits of Grace
– Unlocks diagnostic, clinical, and operational value from medical imaging data: Grace can be used to develop AI applications that allow users to interactively engage with medical images to identify findings, generate alternative imaging views, and suggest surgical interventions and treatment protocols.
– Faster and more cost-effective application development: Grace can be fine-tuned by clients to develop AI applications in about a month, compared to the 12 to 18 months it typically takes.
– Increased image depth: Grace can process images with up to 65,000 shades of grey, compared to the 256 shades that many current AI solutions can only handle.
“We are excited to introduce Grace, a groundbreaking AI platform that empowers developers and healthcare providers to extract transformative insights from medical imaging data,” said Dr. Khan M. Siddiqui, CEO of HOPPR. “Grace represents a significant step forward in our mission to revolutionize medical imaging and improve patient care.”
Addressing Key Obstacles to Optimal AI Use in Medical Imaging
HOPPR developed its foundation model exclusively on AWS using Amazon SageMaker, with plans to utilize AWS HealthImaging, Amazon Bedrock, and other services for data storage, inferencing, and model development in the future as it’s scaled. Working together, the companies aim to address key obstacles to optimal AI use in medical imaging:
– Dynamic Integration: Many current AI solutions for medical imaging do not fully meet the needs of medical imaging professionals. They are static and lack integration with broader patient context. HOPPR enables cross-modality comparison, historical and contextual perspective, real-time prompt and recall, and system-wide treatment planning.
– Faster and More Cost-Effective Application Development: Clinical app developers spend 12 to 18 cost-intensive months training and developing models and equally lengthy periods of integration and deployment. By exposing the Foundation Model for fine-tuning by clients, the development process can be compressed to about a month.
– Increased Image Depth: Most available AI tools were developed by downsampling images, meaning 99% of the data contained in the medical imaging study is not available in traditional training models. Whereas many current AI solutions require downsampling grey scale to 256 shades, HOPPR sees 65,000 shades of grey. HOPPR has developed proprietary vision transformers for its development of the model.
Health2047 Investment
HOPPR has received a $3M funding round led by Health2047, a Silicon Valley-based venture studio founded by the American Medical Association. With this investment, HOPPR joins Health2047’s portfolio of startups reshaping healthcare for the future. Other companies in the portfolio include Evidium, Medcurio, Phenomix Sciences, ScholarRx SiteBridge Research and Zing Health.
To join the HOPPR AI Beta program, please visit their website and sign up for early access.
