MBZUAI researchers developed MedAgentSim, a simulated hospital environment to evaluate AI diagnostic abilities. The simulation uses LLM-powered agents to mimic doctor-patient conversations, providing a dynamic assessment of diagnostic skills. The system includes doctor, patient, and evaluator agents that interact within the simulated hospital, making real-time decisions. Why it matters: This research offers a more realistic evaluation of AI in clinical settings, addressing limitations of current benchmarks and potentially improving AI's use in healthcare.
Fudan University's Zhongyu Wei presented research on social simulation driven by LLMs, covering individual and large-scale social movement simulation. Wei directs the Data Intelligence and Social Computing Lab (Fudan DISC) and has published extensively on multimodal large models and social computing. His work includes the Volcano multimodal model, DISC-MedLLM, and ElectionSim. Why it matters: Using LLMs for social simulation could provide new tools for understanding and potentially predicting social dynamics in the Arab world.
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.
KAUST Ph.D. student Michał Mańkowski won a Poster of Distinction Award at the American Society of Transplant Surgeons (ASTS) 18th Annual State of the Art Winter Symposium for his work on kidney allocation systems. His poster described a simulation for a new kidney allocation system to accelerate organ placement, focusing on marginal quality kidneys. The research involves combinatorial optimization, operation research and management science with healthcare applications, stemming from a collaboration with Johns Hopkins School of Medicine. Why it matters: The research aims to improve organ transplantation efficiency and save lives by optimizing kidney allocation systems, demonstrating the potential of AI and optimization techniques in healthcare.
KAUST's Core Labs provide engineering simulation services and training using state-of-the-art technology. The Supercomputing Core Lab (KSL) at KAUST conducts training workshops in partnership with ANSYS, a market leader in engineering and simulation design software. Since 2017, KSL has conducted five training workshops related to engineering software in partnership with ANSYS, with 230 attendees, including 138 individuals from in-Kingdom institutions outside of KAUST. Why it matters: These workshops strengthen Saudi Arabia's engineering capabilities by providing access to simulation software and training, facilitating collaboration between KAUST, Saudi Aramco, and SABIC.
This study explores fine-tuning large language models (LLMs) for Arabic medical text generation to improve hospital management systems. A unique dataset was collected from social media, capturing medical conversations between patients and doctors, and used to fine-tune models like Mistral-7B, LLaMA-2-7B, and GPT-2. The fine-tuned Mistral-7B model outperformed the others with a BERT F1-score of 68.5%. Why it matters: The research demonstrates the potential of generative AI to provide scalable and culturally relevant solutions for healthcare challenges in Arabic-speaking regions.