AI Framework Detects Abnormal Gait Patterns While Protecting Patient Privacy

In the long human effort to see illness before it speaks, researchers have taught machines to read the language of walking—the subtle rhythms and asymmetries that betray neurological distress—while honoring the privacy of those being observed. A framework published in Nature combines transformer-based vision models with federated learning, achieving over 97% accuracy in detecting abnormal gait without ever centralizing the sensitive patient data that makes such surveillance possible. The work sits at a meaningful threshold: technology capable enough to be clinically useful, and principled enou

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AI Framework Detects Abnormal Gait Patterns While Protecting Patient Privacy

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