MBZUAI Professor Agathe Guilloux developed the SigLasso model to forecast hospitalizations using real-time data from Google and Météo France during the COVID-19 pandemic. The model integrates mobility data and weather patterns to predict hospitalization rates 10-14 days in advance. SigLasso outperformed industry standards like GRU and Neural CDE in reducing reconstruction error. Why it matters: This research demonstrates the potential of AI to improve healthcare resource allocation and crisis management by accurately predicting patient flow using readily available data.
Malaria No More (MNM), Reaching the Last Mile (RLM), and MBZUAI have signed an agreement to expand the Forecasting Healthy Futures (FHF) initiative with a $5 million award from RLM. The initiative aims to address the impact of climate change on malaria and other climate-sensitive infectious diseases. MBZUAI will provide expertise to support the eradication of malaria. Why it matters: This partnership highlights the UAE's commitment to global health and leverages AI to combat climate-sensitive diseases, demonstrating a proactive approach to addressing complex global challenges.
MBZUAI researchers developed FetalCLIP, an AI model trained on 210,000 ultrasound images for fast and reliable interpretation of fetal scans. MBZUAI's President Eric Xing contributed to the General Expression Transformer (GET), an AI foundation model acting as a biological simulator to predict gene behavior. MBZUAI and Carleton University created MedPromptX for quicker disease diagnosis and treatment plans using multimodal AI. Why it matters: These AI advancements from MBZUAI have the potential to revolutionize healthcare in the region and globally, from prenatal care to drug discovery and personalized medicine.
The International Renewable Energy Agency (IRENA) has published a report titled 'From Prediction to Power: Applying Weather, Climate Forecasting, and AI in Renewable Energy'. This publication explores the integration of artificial intelligence with advanced weather and climate forecasting models. It details how these technologies can enhance the efficiency, reliability, and predictability of renewable energy sources, such as solar and wind power. Why it matters: This work highlights the critical role of AI in accelerating renewable energy adoption and achieving global climate goals by transforming intermittent energy sources into more stable and manageable power generation assets.