MBZUAI researchers developed ClinGRAD, a multimodal graph neural network that analyzes genomic data, MRI scans, and clinical information to classify dementia types (Alzheimer's, vascular, etc.). The system addresses the challenge of high misdiagnosis rates (up to 30%) in dementia, where incorrect diagnoses can significantly impact patient life expectancy. ClinGRAD aims to be an interpretable AI system, providing explainability to clinicians. Why it matters: Accurate and early diagnosis of dementia subtypes is crucial for slowing disease progression and improving patient care in the region, where the prevalence of dementia is expected to rise significantly.
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.
MBZUAI researchers presented DEFUSE-MS at MICCAI 2025, a novel AI system for analyzing changes in MRI scans of multiple sclerosis (MS) patients. DEFUSE-MS uses a deformation field-guided spatiotemporal graph-based framework to identify new lesions by reasoning about how the brain has changed. The model constructs graphs of small regions within baseline and follow-up MRIs, linking them across time with edges enriched with learned embeddings of the deformation field. Why it matters: DEFUSE-MS reframes the task from simple "spot the difference" to understanding structural changes, potentially improving the speed and accuracy of MS diagnosis and treatment monitoring.
MBZUAI doctoral student Mai A. Shaaban and colleagues developed MedPromptX, a system that analyzes chest X-rays and patient data to aid lung disease diagnoses. MedPromptX uses multimodal large language models with visual grounding and few-shot prompting, trained on a new dataset of 6,000 patient records (MedPromptX-VQA) derived from MIMIC-IV and MIMIC-CXR. The system addresses the challenge of incomplete electronic health records by leveraging the knowledge embedded in large language models to interpret lab results. Why it matters: This research advances AI-driven medical diagnostics by integrating diverse data sources and addressing data gaps, potentially leading to quicker and more accurate diagnoses.