Rhino Health, a startup leveraging federated learning to connect hospitals and AI developers, today emerged from stealth with $5 million. The company says it will use the funds to further develop its platform that provides access to distributed datasets from diverse group of patients.
The global market for big data analytics in health care was valued at $16.87 billion in 2017 and is projected to reach $67.82 billion by 2025, according to a recent report from Allied Market Research. It’s believed that health care organizations’ implementation of big data analytics might reduce annual costs in the coming years by more than 25%. Better diagnosis and disease predictions, enabled by AI and analytics, can lead to cost reduction by decreasing hospital readmission rates, among other factors.
Rhino, which was cofounded by a former exec at Mass General Brigham and an ex-Google engineer who led the Google Duplex team, aims to power AI models through a federated learning approach that ultimately improve the standard of care. In machine learning, federated learning entails training models across decentralized devices that hold data samples (e.g., imaging data, pathology data, structured clinical data, and clinical notes) without exchanging those samples. A centralized server might be used to orchestrate the steps of the algorithm and act as a reference clock, or the arrangement might be peer-to-peer. Regardless, local algorithms are trained on local data samples, and the weights (the learnable parameters of the algorithms) are exchanged between the algorithms at some frequency to generate a global model.
Federated learning isn’t exactly new to the world of medicine. Last June, major pharmaceutical companies inked an agreement to build federated learning technologies to collectively train drug discovery AI on datasets without having to share proprietary data. For its part, Intel is engaged with a National Institutes of Health-funded program that will leverage AI to identify brain tumors while preserving privacy. And Nvidia has begun working with collaborators to release COVID-19-related models trained with federated learning through the company’s Clara Imaging Software platform, following a collaboration with King’s College London on a federated learning neural network for brain tumor segmentation.
Rhino claims its platform enables customers to develop, validate, monitor, and maintain AI models by connecting datasets and developers, ensuring layers of protection. Data is anonymized and remains behind the local firewall. Using Rhino, managers can create “regulatory-grade” data packages from model validation and monitor data streams while identifying opportunities to improve performance and generalizability. Beyond this, Rhino can help to transition prototype models from the research phase to the regulatory approval and eventually the clinical deployment stages.
CTO Yuval Baror notes that as AI solutions proliferate throughout the health industry, there’s increasing attention on how they’re developed and maintained. In January 2021, the U.S. Food and Drug Administration updated its action plan for AI and machine learning in software as a medical device, underscoring the importance of inclusivity across dimensions like sex, gender, age, race, and ethnicity when compiling datasets for training and testing.
“Rhino Health is bringing together foundational learnings and emerging best practices from AI-forward industries to ensure that healthcare solutions are solving real-world problems and delivering consistent results,” Baror said. He added that Rhino has become a member of Nvidia’s Inception program in a collaboration with the chipmaker to bring its federated learning solution to clinics. “With federated learning, we’re able to do this in the privacy-centric manner this industry demands, advancing the interests of patients, hospitals, and technology developers alike.”
LionBird Ventures led the seed round in Rhino announced today, which had participation from Arkin Holdings and several angel investors. Rhino is headquartered in Cambridge, Massachusetts, with an R&D center in Tel Aviv.
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