Chipmaker Nvidia sees big opportunities in healthcare AI and this week it launched more than two dozen new gen AI-powered microservices.
The global computing powerhouse also inked collaborations with Abridge, Microsoft, GE HealthCare and Hippocratic AI to to expand its generative AI capabilities into healthcare and life sciences.
Nvidia, a leader in AI computing, launched this week more than two dozen new microservices to enable healthcare enterprises to take advantage of the latest advances in generative AI in the areas of drug discovery, med tech and digital health.
The new suite of NVIDIA healthcare microservices includes optimized NVIDIA NIM AI models and workflows with industry-standard application programming interfaces, functioning as building blocks for the development and deployment of cloud-native applications.
These microservices, accessible on any cloud platform, offer specialized capabilities like imaging, natural language processing, speech recognition, and digital biology simulation. The company says its AI microservices can be used to screen for trillions of drug compounds to advance medicine, gather better patient data to aid early disease detection and implement smarter digital assistants.
The announcement was made during Nvidia’s GTC conference this week in San Jose, California.
"For the first time in history, we can represent the world of biology and chemistry in a computer, making computer-aided drug discovery possible,” said Kimberly Powell, vice president of healthcare at Nvidia, in a statement. “By helping healthcare companies easily build and manage AI solutions, we’re enabling them to harness the full power and potential of generative AI.”
Abridge
Abridge, a startup that develops generative AI tech for clinical documentation, is collaborating with Nvidia to use its compute resources, foundation models and expertise in efficiently deploying AI systems to bolster its work.
Nvidia's computing power will support the company's research and help it scale a multilingual clinical conversation platform across the entire U.S. healthcare system, executives said.
NVIDIA’s venture capital arm, NVentures, also invested in the company as part of its $150 million series C round back in February.
"This collaboration will help us to redefine the possibilities for clinically specialized foundation models, advance medical speech and language technology, and innovate in the space of responsible AI,” said Zachary Lipton, chief technology and scientific officer of Abridge and the Raj Reddy Assistant Professor of Machine Learning at Carnegie Mellon University. “We’re also working with NVIDIA on core research efforts and plan to explore novel collaborative research in healthcare applications that could improve the lives of clinicians and patients every day.”
Abridge continues to rapidly grow and ink new partnerships with health systems to deploy its technology, including, most recently, UCI Health, the health system of the University of California, Irvine. The deployment at UCI Health will be focused on reducing the burden of clinical documentation and improving operational efficiency for clinicians across the system. Yale New Haven Health System, Emory Healthcare, The University of Kansas Health System, UPMC, and dozens of other health systems also are using Abridge's AI-based clinical documentation technology.
GE HealthCare
Healthcare tech company GE HealthCare is teaming up with Nvidia to bring AI to ultrasound. The company's is using Nvidia's technology to develop an AI-powered research model called SonoSAMTrack.
SonoSAMTrack combines a promptable foundation model for segmenting objects on ultrasound images called SonoSAM1. It segments anatomies, lesions, and other essential areas in ultrasound images.
In a recent study conducted by GE HealthCare, its research project, SonoSAMTrack, showcased high performance across seven ultrasound datasets, encompassing a wide range of anatomies (adult heart and fetal head) and pathologies (breast lesions and musculoskeletal pathologies), as well as different scanning devices. Notably, it outperformed competing methods by a substantial margin. In addition, SonoSamTrack exhibited enhanced performance metrics in terms of speed and efficiency, requiring only two to six clicks for precise segmentation, thus minimizing user input.
"Our vision is to accelerate advancements in medical imaging by introducing foundational AI technologies, thereby empowering data scientists to expedite AI application development and eventually help clinicians and enhance patient care. By utilizing these versatile, generalist models, we aim to adapt more efficiently to new tasks and medical imaging modalities, often requiring far less labeled data compared to the traditional model retraining approach. This is particularly significant in the healthcare domain, for which data is especially time-consuming and costly to obtain,” said Parminder Bhatia, Chief AI Officer at GE HealthCare in a statement.
GE HealthCare and Nvidia have a long-term AI collaboration. Nvidia also has worked with a number of medtech companies to incorporate AI into their technologies.Johnson & Johnson MedTech this week announced it was collaborating with the company to help deliver real-time analyses of surgical data.
Nvidia plans to offer the expanded BioNeMo models as a series of cloud-based enterprise services—with predictors for drug molecule binding successes and changes in protein structures—hosted through its AWS HealthOmics platform, Fierce Medtech reported.
Microsoft
Tech giant Microsoft is expanding its collaboration with Nvidia to advance the use of generative AI, the cloud and accelerated computing to healthcare and life sciences organizations. The collaboration will bring together the advanced computing capabilities of Microsoft Azure with Nvidia DGX Cloud and the Nvidia Clara suite of computing platforms, software and services, the companies said.
The collaboration aims to accelerate clinical research and drug discovery, enhance medical image-based diagnostic technology, and increase access to precision medicine.
The companies will work to combine Azure, NVIDIA MONAI (Medical Open Network for AI), and the Nuance Precision Imaging Network (PIN) to enable the development, validation, deployment and evaluation of medical imaging AI models at scale. With the combined offering, developers can build highly performant medical imaging AI models, healthcare providers can deploy a single PIN platform for running a wide array of third-party AI models integrated into clinical workflows, and clinical researchers can accelerate drug discovery.
For example, Mass General Brigham AI leveraged MONAI and PIN in the aftermath of COVID-19 to support a federated approach to build models capable of assessing lung function and predicting oxygen requirements for symptomatic patients.
The two companies also are working to make a future suite of Nvidia healthcare microservices available on Azure AI for optimized inference and fine-tuning. Once available, researchers, clinicians and developers can explore these state-of-the-art models and deploy them as endpoints in the Azure AI Model Catalog, the companies said.
Hippocratic AI
Startup Hippocratic AI is collaborating with Nvidia to use its AI platform to develop empathetic AI healthcare agents to enable super-low-latency conversational interactions. User tests repeatedly show that super low latency voice interaction is required for patients to build an emotional connection naturally. Since LLMs run on inference engines, Hippocratic AI has termed this low latency inference: “Empathy Inference.”
At Nvidia GTC, a global AI developer conference, the two companies showcased the potential of a generative AI healthcare agent avatar with the Nvidia Avatar Cloud Engine suite of technologies, which brings digital humans to life with generative AI.
The agent calls patients on the phone, follows up on care coordination tasks, delivers preoperative instructions and performs post-discharge management.