Healthcare

Medical experts use NVIDIA-powered federated learning to enhance AI models for tumour segmentation, exchanging models instead of data across institutions.

MIT's ScribblePrompt AI speeds up medical image analysis by 28%. Trained on 50,000+ simulations, it highlights structures via user scribbles across scan types.

DPAD AI algorithm isolates specific brain patterns from complex neural activity. It prioritizes learning behavior-related patterns, enhancing brain-computer interfaces.

NVIDIA's NIM Agent Blueprint integrates AI models for virtual drug screening, aiming to reduce development time and costs in pharmaceuticals.

University of Cologne's AI digital pathology platform analyses lung cancer tissue samples faster and more accurately, aiding diagnosis and treatment decisions.

Google's HeAR AI model analyses cough sounds to detect diseases, trained on 300 million audio samples. It shows promise for early TB detection, outperforms other models, and is available to researchers for developing custom bioacoustic tools.