Dr. Mohammad Yaqub, an Assistant Professor at MBZUAI, leads the BioMedIA lab and focuses on applying AI to real-world healthcare challenges, particularly in smart imaging. He was inspired by a textbook by Tom Mitchell and his work at Oxford University where he helped develop ScanNav, an AI solution aiding sonographers in anomaly scans during pregnancy. ScanNav assists in assessing fetal growth and detecting abnormalities, potentially improving early intervention. Why it matters: This highlights the growing importance of AI in enhancing medical diagnostics and improving healthcare outcomes in the UAE and globally.
MBZUAI Associate Professor Mohammad Yaqub is focused on translating AI research into real-world healthcare solutions. His previous work includes the development of SanNav, an AI-based fetal anomaly detection system that became an FDA-approved product used by GE Healthcare and used on his own wife during pregnancy. Yaqub joined MBZUAI to help build a new model of AI research and education with a focus on interdisciplinary collaboration and industry partnerships. Why it matters: This highlights the UAE's growing focus on AI in healthcare and MBZUAI's role in bridging the gap between research and practical applications in the medical field.
Dr. Mohammad Yaqub from MBZUAI will present AI solutions used to combat the COVID-19 pandemic, addressing healthcare consequences, social, economic, and policy-making decisions. The talk will cover the applications of AI and also discuss challenges like privacy, data needs, generalizability, data noise, and human acceptance. Yaqub's background includes a DPhil from the University of Oxford in Biomedical Engineering and research at the Institute of Biomedical Engineering, focusing on machine learning solutions for medical problems. Why it matters: This talk highlights the important role of AI in addressing pandemics and the ethical considerations that come with its application in healthcare and policymaking.
MBZUAI's BioMedIA lab, led by Mohammad Yaqub, is developing AI solutions for healthcare challenges in cardiology, pulmonology, and oncology using computer vision. Yaqub's previous research analyzed fetal ultrasound images to correlate bone development with maternal vitamin D levels. The lab is now applying image analysis to improve the treatment of head and neck cancer using PET and CT scans. Why it matters: This research demonstrates the potential of AI and computer vision to improve diagnostic accuracy and accessibility of healthcare in the region and beyond.
MBZUAI's Dr. Mohammad Yaqub is developing AI algorithms to power point-of-care ultrasound (PoCUS) on mobile devices, expanding on his prior work on an AI-based fetal anomaly system used in GE Healthcare's ultrasound. These algorithms aim to make smaller, affordable PoCUS devices accessible in remote areas for faster diagnoses. The handheld devices, costing around $5000 USD, can connect to mobile devices and provide intelligence to interpret images, addressing the shortage of specialists in remote locations. Why it matters: This initiative democratizes access to critical diagnostic tools, potentially saving lives by enabling early detection of life-threatening conditions in underserved communities.
Professor Mohammad Younis, a new Associate Professor of Mechanical Engineering at KAUST, focuses his research on micro and nanotechnology, specifically the interface between nonlinear dynamics and micro/nano electromechanical systems (MEMS and NEMS). He is developing a generic platform for sensing and actuation with potential uses in detecting poisonous gases, biohazards, and earthquake signals. He is also working on actuator systems that can assist elderly people after a fall by automatically calling for help. Why it matters: This research has significant implications for safety, environmental monitoring, and elderly care in the Middle East and beyond.
MBZUAI hosted an AI Talks session featuring Dr. Mohammad Yaqub discussing AI's role in fighting COVID-19 and predicting future pandemics. AI can detect outbreaks by mining news and social media for unusual patterns, as demonstrated by companies flagging pneumonia cases in Wuhan before the official announcement. AI-empowered drug repurposing identified Baricitinib as a potential COVID-19 treatment and can predict virus mutations. Why it matters: This highlights the potential of AI to enhance pandemic preparedness and response in the region, particularly through institutions like MBZUAI.
MBZUAI students and researchers presented findings at the Graduate Student Research Conference (GSRC) in Dubai, led by Assistant Professor Mohammad Yaqub. Topics included deep learning, computer learning, disease prediction, and AI in healthcare, with students from the BioMedIA lab presenting their work. Presentations covered areas like fetal ultrasound quality assessment, head and neck cancer diagnosis, and disease risk prediction using generative pre-trained transformers. Why it matters: This showcases MBZUAI's focus on applying AI to solve real-world healthcare problems and highlights the contributions of its students in advancing medical AI research.