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.
The paper introduces Aladdin-FTI, a system designed for generating and translating dialectal Arabic (DA). Aladdin-FTI supports text generation in Moroccan, Egyptian, Palestinian, Syrian, and Saudi dialects. It also handles bidirectional translation between these dialects, Modern Standard Arabic (MSA), and English. Why it matters: This work contributes to addressing the under-representation of Arabic dialects in NLP research and enables more inclusive Arabic language models.
The Technology Innovation Institute (TII) in Abu Dhabi has launched a 2-micrometer high-power fiber laser for medical and industrial applications. Developed by TII's Directed Energy Research Center, the Thulium-based laser is efficient, compact, and scalable, enabling precise interaction with water-rich materials. TII has partnered with LIMA Photonics, a German MedTech startup, to integrate the laser into clinical solutions, including urinary stone treatment and prostate surgery. Why it matters: This laser technology and partnership showcase the UAE's commitment to translating advanced research into healthcare solutions, positioning Abu Dhabi as a hub for medical technology innovation.
MBZUAI researchers have introduced MIRA, a novel framework for improving the factual accuracy of multimodal large language models in medical applications. MIRA uses calibrated retrieval to manage factual risk and integrates image embeddings with a medical knowledge base for efficient reasoning. Evaluated on medical VQA and report generation benchmarks, MIRA achieves state-of-the-art results, with code available on GitHub.
Professor Marco Amabili, advisor at the Advanced Materials Research Center (AMRC), received the 'Cataldo Agostinelli and Angiola Gili Agostinelli' International Prize from the Lincei National Academy of Sciences of Italy. The award recognizes Prof. Amabili's research in mechanical vibrations, composite structures, and vascular biomechanics. He received the award in Rome from Nobel laureate Professor Giorgio Parisi. Why it matters: The recognition highlights the growing international visibility of UAE-based researchers and the increasing commitment of UAE institutions like TII to deep-tech research.
TII's DERC, in partnership with Brazilian firm RADAZ, has obtained the first microwave images from their joint project on Airborne Multi-band Interferometric Microwave Imaging (A(MI)2) in Abu Dhabi. The project uses a new multiband Synthetic Aperture Radar (SAR) operating in P, L, and C frequency bands to generate terrain images. The system, which can be mounted on commercial drones, also integrates Ground Penetrating Radar capability to detect buried objects. Why it matters: This technology enhances remote sensing capabilities in the region, enabling applications in agriculture, infrastructure monitoring, and search and rescue operations.
MBZUAI researchers Darya Taratynova and Shahad Hardan developed Forget-MI, a method for making clinical AI models "unlearn" specific patient data without retraining the entire model. Forget-MI addresses the challenge of removing patient data from AI models trained on multimodal records (like chest X-rays and reports) due to regulations like GDPR and HIPAA. The method unlearns both unimodal (image or text) and joint (image-text) associations while retaining overall accuracy using a late-fusion multimodal classifier. Why it matters: This research provides a practical solution to a critical privacy concern in healthcare AI, enabling compliance with data protection regulations and fostering trust in AI-driven medical applications.