MBZUAI researchers led by Dr. Mohammad Yaqub are developing AI algorithms for real-time medical diagnoses, including tools for multiple sclerosis and congenital heart disease. The team developed ScanNav, an AI fetal anomaly assessment system licensed by GE Healthcare for Voluson SWIFT ultrasound machines. ScanNav assists doctors during anomaly scans after 20 weeks of gestation to check for conditions like heart issues and spina bifida. Why it matters: This research has the potential to significantly improve the speed and accuracy of medical diagnoses in the UAE and beyond, addressing critical gaps in healthcare.
MBZUAI doctoral student Mai A. Shaaban and colleagues developed MedPromptX, a system that analyzes chest X-rays and patient data to aid lung disease diagnoses. MedPromptX uses multimodal large language models with visual grounding and few-shot prompting, trained on a new dataset of 6,000 patient records (MedPromptX-VQA) derived from MIMIC-IV and MIMIC-CXR. The system addresses the challenge of incomplete electronic health records by leveraging the knowledge embedded in large language models to interpret lab results. Why it matters: This research advances AI-driven medical diagnostics by integrating diverse data sources and addressing data gaps, potentially leading to quicker and more accurate diagnoses.
KAUST researchers suggest antibody testing can complement PCR tests to reduce false negatives in COVID-19 diagnosis. PCR tests can produce false negative results. Immunodiagnostic tests could help identify unknowingly spreading the disease. Why it matters: Improving diagnostic accuracy is critical for effective pandemic control and public health management in Saudi Arabia and globally.
KAUST Professor Xin Gao formed part of the Rapid Research Response Team (R3T) to address the COVID-19 pandemic. Gao's team developed and deployed an AI system to assist clinicians in improving the accuracy of COVID-19 diagnoses. The lecture outlines how the AI system was built and implemented. Why it matters: This showcases how GCC academic institutions are leveraging AI to address pressing healthcare challenges.
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 and Sheikh Shakbout Medical City researchers developed PECon, a deep learning method for pulmonary embolism detection using CT scans and electronic health records. PECon uses neural networks and contrastive learning to encode and align image and text data. The method aims to improve diagnosis accuracy and speed, potentially saving lives. Why it matters: This research demonstrates AI's potential to enhance medical diagnostics in the UAE, addressing a critical healthcare challenge.
MBZUAI researchers are refining AI techniques to improve cancer diagnosis for colorectal and breast cancer, both common in the Middle East. They are using "few-shot tissue image generation," in which AI generates data for training AI models to recognize lesions, addressing the challenge of limited training data. The developed framework improves the efficiency of radiologists in breast cancer diagnosis, leading to better detection of breast lesions and timely treatment interventions. Why it matters: These advancements in AI-aided diagnostics can lead to earlier and more accurate cancer detection, ultimately improving patient outcomes in the region and beyond.
A senior lecturer at the University of New South Wales discussed the use of AI to improve early prognosis and personalized treatment plans for neurodegenerative diseases, cardiovascular imaging and multiomics. The lecture highlighted the potential of AI algorithms to detect subtle changes at early stages through advanced multiomics techniques and medical imaging analysis. The speaker has expertise in analyzing medical images and has collaborated with medical professionals to develop AI tools for diagnosis of cancer, neurodegenerative disease, and heart disease. Why it matters: AI-driven prognosis and treatment planning promises earlier intervention and improved outcomes for challenging diseases in the region.