An AI tool has reportedly been developed that can detect pancreatic cancer up to three years before a clinical diagnosis. This finding, based on a new study, was highlighted in a report by The National. The tool aims to significantly improve early detection capabilities for a challenging disease. Why it matters: Early and accurate detection of pancreatic cancer could lead to earlier interventions and substantially improve patient outcomes and survival rates.
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.
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.
KAUST faculty received multiple gold and silver medals at the Geneva International Exhibition of Inventions 2025. Professor Dana Alsulaiman won the IFIA Best Invention Award for "Bio-MXenes," a portable biosensor for detecting microRNA cancer biomarkers from liquid biopsies. Other awarded projects included super-resolution imaging of ferromagnetic tubulars and rapid Zika virus detection. Why it matters: The awards highlight KAUST's role as a hub for groundbreaking research, especially in medical diagnostics and AI-enhanced imaging.
Professor Sahika Inal, associate professor of bioengineering at KAUST, has been named a Fellow of the Royal Society of Chemistry (RSC). This recognizes her work in designing electronic devices for efficient communication with biological systems. Inal's work involves organic electronic materials and devices for research, clinical health monitoring, and therapy. Why it matters: This fellowship elevates KAUST's profile in biomedical engineering and highlights the importance of interdisciplinary research in addressing critical healthcare challenges.
KAUST alumnus Zhenwei Wang (Ph.D. '18), who studied under Professor Husam Alshareef, focused on developing oxide semiconductors for transparent electronics during his time at KAUST. Currently a postdoctoral researcher at the Okinawa Institute of Science and Technology (OIST), he is now developing novel biological sensing devices using nanoparticles. Wang credits KAUST's facilities and support for enabling him to overcome research challenges. Why it matters: The story highlights KAUST's role in fostering materials science talent and contributing to advancements in bio-sensing technology, with implications for future medical diagnostics.
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.