Common Disease

Research on common diseases like Alzheimer's and Parkinson's is intensely focused on developing accurate and accessible diagnostic tools using machine learning. Current efforts leverage multimodal data (MRI, EEG, genomics, voice analysis) and advanced architectures like graph neural networks, convolutional neural networks, transformers, and state space models to improve diagnostic accuracy and identify disease subtypes. These advancements aim to enable earlier diagnosis, personalized treatment strategies, and improved patient outcomes, particularly in resource-limited settings. Furthermore, research emphasizes the development of robust and interpretable models that address data privacy and bias concerns within healthcare applications.