AI, when applied to healthcare, holds the promise of better medical predictions and faster medicinal improvements. To do so, it needs data on which to train. But healthcare data is sensitive and private, creating a dead end.
Or not. Walter De Brouwer, CEO of Silicon Valley startup doc.ai, joined AI Podcast host Noah Kravitz to discuss how his medical research-based platform makes the application of deep learning to healthcare possible.
Many consumers worry about the outcome of putting their data in the cloud, where it risks being pirated. And larger institutions like hospitals that have an abundance of healthcare data are reticent to share it because it could reveal other sensitive business information.
Doc.ai first collects everything it needs from the device and user input. It takes into account data from Apple Health, blood tests, urine analysis, and any other medical information uploaded by the client.
The platform has eight prediction models, which exist locally on the client’s device. Each module has a specific focus, from the general medical record, to urine sampling, to phenomics.
These models transform the data into tensors — what De Brouwer calls “a big pile of numbers.” They’re then uploaded to the cloud with no risk of being pirated, because without the model that produced them, they’re like “GPS coordinates on planet Jupiter – you can’t do anything with it.”
From there, doc.ai can use these tensors to improve its deep learning algorithms and improve medical predictions.
Doc.ai also provides a platform to run medical trials. Clients and doctors can create their own, or take part in the three that De Brouwer has already organized. The first is organized in collaboration with advisors from Harvard Medical School, and focuses on allergy triggers. Another, studying the various combinations of the 26 medicines designed to treat epilepsy, was designed alongside Stanford specialists.
De Brouwer is no stranger to entrepreneurship. He’s the cofounder of several AI-based companies, including Inui Health, which performs urine analysis using machine learning and mobile phones, and XY.ai, a spinoff of Harvard Medical School that uses AI for large-scale digital twin technology.
De Brouwer is certain that doc.ai has found the ideal approach for improving healthcare knowledge. “This is Darwinism,” he says. “First you collect, then you predict, and then you change the bad things and amplify the good things. These three steps are basically the evolution algorithm of the planet.”
To learn more about doc.ai, a member of NVIDIA’s Inception startup accelerator program, visit their website here. Or visit their blog for weekly stories, as well as Github commands and Jupyter notebooks.
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