"AI is much more than a buzzword in healthcare. It is the underpinning of the next generation of technologies that will change the practice of medicine," said Dr. Dayan. "To deliver on that promise, though, AI models must be created and trained using data that represents the real-world patient populations. We're increasing access to diverse datasets, as that is what will ultimately make AI-based healthcare solutions widely available to and effective for the entire population of clinicians and patients who need them."

"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," said Yuval Baror, co-founder and CTO of Rhino Health. "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."

Utilizing Federated Learning, Rhino Health eliminates the complexity, expense and risk of moving and managing huge volumes of data. An AI developer's model is applied to patient data where it lives. With this approach, AI models quickly and continuously improve - learning from each new dataset and applying those learnings to the next. This accelerates creation of more accurate AI models that work consistently across different patient populations, which leads to more widespread adoption of advanced healthcare solutions.

As AI-based solutions proliferate across the health ecosystem, there is increasing attention on how they are developed, utilized, maintained and measured throughout the full product lifecycle. In January 2021, the FDA updated its Action Plan for "Artificial Intelligence and Machine Learning in Software as a Medical Device," underscoring the importance of inclusivity - across dimensions such as sex and gender, age, race and ethnicity - when assembling datasets for training and validation of AI devices.

"The power of Federated Learning in healthcare is immense, and a silver lining of the pandemic is that more people across the health ecosystem are increasingly aware of and interested in this approach," said Fiona Gilbert, MD, chair of Radiology at the University of Cambridge School of Medicine, an author of "Federated Learning used for predicting outcomes in SARS-COV-2 patients," and a member of Rhino Health's Advisory Board. "Looking ahead, Federated Learning has the potential to deliver large-scale impact across diagnostic imaging and digital pathology, bringing together healthcare providers and technology developers in a way never before possible."

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