MBZUAI valedictorian Salma Hassan developed a multimodal graph learning approach for early dementia diagnosis and prognosis. Her master's thesis focused on differential diagnosis to identify specific dementia subtypes and predicting cognitive decline in preclinical patients. The AI model leverages brain imaging, genetics, and clinical records to improve diagnostic accuracy. Why it matters: This research can improve early detection and resource allocation for dementia management, especially in developing countries.
A senior lecturer at the University of New South Wales discussed the use of AI to improve early prognosis and personalized treatment plans for neurodegenerative diseases, cardiovascular imaging and multiomics. The lecture highlighted the potential of AI algorithms to detect subtle changes at early stages through advanced multiomics techniques and medical imaging analysis. The speaker has expertise in analyzing medical images and has collaborated with medical professionals to develop AI tools for diagnosis of cancer, neurodegenerative disease, and heart disease. Why it matters: AI-driven prognosis and treatment planning promises earlier intervention and improved outcomes for challenging diseases in the region.