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.
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.
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.
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.
Jorge Amador, a PhD student at KAUST's Visual Computing Center, presented a talk on physically-based simulation for generative AI models. The talk covered the use of synthetic data generation and physical priors to address the need for high-quality datasets. Applications discussed include photo editing, navigation, digital humans, and cosmological simulations. Why it matters: This research explores a promising technique to overcome data scarcity issues in AI, particularly relevant in resource-constrained environments or for sensitive applications.
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.
MBZUAI's Professor Le Song is developing an AI-driven simulation to model the human body at societal, organ, tissue, cellular, and molecular levels. The goal is to reduce the time and cost associated with bringing new medicines to market by removing the need for wet lab biological research. Song aims to create a comprehensive model using machine learning. Why it matters: This research could revolutionize drug discovery in the region by accelerating the development process and reducing reliance on traditional research methods.
KAUST Ph.D. student Michał Mańkowski's research on kidney allocation strategies was recognized as one of the American Journal of Transplantation's "Top 10 Articles of 2019." The research demonstrated how an accelerated allocation strategy could increase the utilization of kidneys at risk for non-use, potentially reducing discard rates. Mańkowski aims to translate his U.S.-focused research to improve organ transplantation within the Saudi Arabian healthcare system. Why it matters: This research has the potential to improve organ transplant outcomes and resource allocation in Saudi Arabia, addressing a critical healthcare need.