MBZUAI hosted a panel discussion in collaboration with the Manara Center for Coexistence and Dialogue. The discussion focused on the intersection of AI and medical image computing. Jiebo Luo, a professor at the University of Rochester, discussed his work on applying AI to healthcare, including moving beyond classification to semantic description and expanding use from hospitals to home telemedicine. Why it matters: This highlights the increasing focus on AI applications in healthcare within the Middle East, particularly at institutions like MBZUAI, which are fostering discussions on the ethical and practical implications of AI in medicine.
MBZUAI researchers developed Human-in-the-Loop for Prognosis (HuLP), a new AI system designed to help physicians assess cancer progression by providing information about its predictions and allowing user intervention. The system aims to foster collaboration between physicians and AI, rather than replacing doctors. It was presented at the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Why it matters: This research highlights the potential of AI to augment physician expertise in critical areas like cancer prognosis, improving patient care and treatment decisions.
Researchers at MBZUAI have developed a new machine learning method called survival rank-n-contrast (SurvRNC) to improve survival models for cancer prognoses. The method is designed to predict survival times for head and neck cancer patients using multimodal data while accounting for censored data (missing values). Numan Saeed presented the team’s work at the 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Why it matters: Accurate prognoses can significantly improve patient outcomes, and this research contributes to advancements in machine learning techniques for handling complex and incomplete medical data.
MBZUAI faculty, researchers, and students presented eight academic papers at the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022) in Singapore. Seven of the accepted papers feature a master’s or doctoral student as first author. The papers are the outcome of two MBZUAI faculty led labs – BioMedical Image Analysis (BioMedIA) lab and SPriNT-AI. Why it matters: This highlights MBZUAI's growing prominence in medical image analysis and AI, showcasing the university's commitment to producing high-quality research and fostering young talent in the field.