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.
KAUST is supporting Saudi Arabia's healthcare modernization by translating laboratory research into smart, digital, and precise solutions. One example is the Social and Personal Adaptive Response Kit (SPARK), an AI-driven technology integrating behavioral analysis, wearable multi-sensor systems, and human body communication to support children with autism. KAUST researchers have also developed a fully printed wireless electrocardiogram system and a smart bandage for various applications. Why it matters: These innovations align with Saudi Vision 2030 and have the potential to improve healthcare outcomes in Saudi Arabia and globally through personalized, remote care.
KAUST and Lean Business Services (the digital arm of the Saudi health sector) have signed a Memorandum of Understanding to advance AI and data science for a "smart health" ecosystem in Saudi Arabia. During the COVID-19 pandemic, they collaborated to provide the Saudi Ministry of Health with AI-driven trends and analysis. KAUST's Shaheen II supercomputer was utilized to process healthcare data. Why it matters: This partnership aims to develop AI and advanced analytics solutions to enhance healthcare access, quality, and cost-efficiency in line with Saudi Vision 2030.
MBZUAI researchers are developing AI applications for malaria prevention in Indonesia using sensory data fusion and digital twins. Another MBZUAI team is using machine learning and computer vision to detect cardiovascular disease from CT scans in collaboration with the University of Oxford. AI-powered remote patient monitoring is also being explored for proactive interventions and chronic disease management. Why it matters: These projects demonstrate the potential of AI to address healthcare challenges in underserved communities and improve disease prevention and management in the region.
MBZUAI researchers developed FeSViBS, a new federated split learning technique for vision transformers that addresses data scarcity and privacy concerns in healthcare image classification. The method combines federated learning and split learning to train models collaboratively without sharing sensitive patient data directly. It overcomes limitations of traditional centralized training and vulnerabilities in federated learning. Why it matters: This approach enables the development of AI-powered healthcare applications while adhering to stringent data privacy regulations, unlocking the potential of machine learning in medical imaging.
Ehsan Hoque from the University of Rochester gave a talk at MBZUAI discussing how to integrate AI into healthcare to improve access and equity. He emphasized that technology should align with values and infrastructure, advocating for AI solutions developed through collaboration between computer scientists and healthcare professionals. Hoque presented examples like using AI to quantify movement disorders and improve empathy skills. Why it matters: This highlights the importance of human-centered AI development in the GCC region, particularly in sensitive sectors like healthcare, and MBZUAI's role in fostering such discussions.
A report discusses using AI to optimize healthcare delivery across the entire medical process cycle, including pre-hospital screening, in-hospital treatment, and post-hospital rehabilitation. It considers optimal management of workflow, medical resources, and comprehensive healthcare coverage. Dr. Jingshan Li from Tsinghua University is the author, with extensive publications and experience in production and healthcare systems. Why it matters: AI-driven improvements to healthcare processes could lead to better resource allocation and enhanced patient outcomes across the GCC region.
The Abu Dhabi Department of Health (DoH) has partnered with MBZUAI and Core42 to launch the Global AI Healthcare Academy. The academy will provide AI training to healthcare workers in Abu Dhabi, aiming to improve diagnostics, operational efficiency, and patient care. The initiative includes mass training sessions for up to 100 attendees and customized courses in areas like AI for radiology and cardiology. Why it matters: This partnership signals a strategic push to integrate AI into Abu Dhabi's healthcare system, potentially establishing the emirate as a leader in technology-driven healthcare.