Hospitals and healthcare institutes have incredibly valuable patient and research participant data that could drive major medical breakthroughs. But, it’s private and sensitive that sharing it safely is a huge challenge under strict privacy laws. To solve this, researchers from MetaHealth explored Federated Learning (FL), a method where institutes train computational/AI models on their own private data and only share the learned insights (like updated model instructions) with each other, never the raw patient records.
They have developed a tool called “FedDeepInsight” that transforms complex medical tables into images (since AI is great at analyzing pictures), making the FL process more accurate. Testing showed that adding Differential Privacy (like carefully controlled digital static) ensured no individual patient details could be figured out from the shared information, creating a promising way to unlock medical data’s power while keeping it completely secure and private.
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