Microsoft has teamed up with Mass General Brigham, the University of Wisconsin School of Medicine and Public Health, and UW Health to address major radiology challenges and enhance AI in medical imaging.
The collaboration aims to enhance clinician efficiency and health outcomes using high-performance multimodal AI foundation models on the Microsoft Azure AI platform.
Also, the partnerships will extend a suite of radiology applications from Nuance, a subsidiary of Microsoft, to enable a wide array of high-value medical imaging copilot applications.
Microsoft said that the collaborations will explore advanced algorithms and applications to help radiologists interpret images, generate reports, classify diseases, and analyse structured data.
Microsoft Health and Life Sciences corporate vice president Peter Durlach said: “We are proud to announce our expanded collaborations with leading institutions like Mass General Brigham and UW.
“Along with other industry partners, our joint efforts aim to leverage the power of imaging foundation models to improve experiences and workflow efficiency across the radiology ecosystem in a way that is reliable, transparent, and secure.
“Together, we are not only advancing medical imaging but also helping deliver more accessible and better-quality patient care in a very resource-constrained environment.”
The researchers from Mass General Brigham, the University of Wisconsin, and UW Health will collaborate with Microsoft to develop and validate advanced multimodal foundation AI models.
It includes deploying real-world use cases into clinical workflows via platforms like Nuance’s PowerScribe and the Nuance Precision Imaging Network, said Microsoft.
Mass General Brigham chief data science officer and chief imaging officer and Mass General Brigham AI business leader Keith Dreyer said: “Generative AI has transformative potential to overcome traditional barriers in AI product development and to accelerate the impact of these technologies on clinical care.
“As healthcare leaders, we need to carefully and responsibly develop and evaluate such tools to ensure high-quality care is in no way compromised.
“Foundation models fine-tuned on Mass General Brigham’s vast multimodal longitudinal data assets can enable a shorter development cycle of AI/ML-based software as a medical device and other clinical applications.”
