AI applications in healthcare across the GCC — including medical imaging, clinical decision support, genomics, and health informatics from Gulf universities and hospitals.
Professor Cesare Stefanini will discuss the relationship between AI and natural systems, highlighting robotics inspired by nature. His talk will cover neuro-inspired robot control, bio-hybrid ecosystems, and advancements in biomedical and industrial robotics. Stefanini will share achievements in underwater robot locomotion, AI-powered biomedical systems, and industrial platforms enhancing human manipulation. Why it matters: The talk at Khalifa University reflects the UAE's interest in bio-robotics research and integrating AI into various sectors, potentially fostering collaborations and advancements in the field.
This article discusses a talk on "Assistive Augmentation," designing human-computer interfaces to augment human abilities. Examples include 'AiSee' for blind users, 'Prospero' for memory training, and 'MuSS-Bits' for deaf users to feel music. Suranga Nanayakkara from the National University of Singapore will present the talk, highlighting insights from psychology, human-centered machine learning, and design thinking. Why it matters: Such assistive technologies can significantly improve the quality of life for individuals with disabilities and extend human capabilities.
This article discusses the need for a decentralized approach to AI, especially in contexts where data and knowledge are distributed. It highlights five key technical challenges: privacy, verifiability, incentives, orchestration, and crowdUX. The author, Ramesh Raskar from MIT Media Lab, advocates for integrating privacy tech, distributed verifiable AI, data markets, orchestration, and crowd experience into the Web3 framework. Why it matters: Decentralized AI could unlock new possibilities for collaboration and problem-solving in the region, particularly in sectors like healthcare and logistics where data is often siloed.
Tetsunari Inamura's talk explores using VR to collect HRI data and tailor assistive robotic functionalities to individual users. He discusses symbol emergence via multimodal interaction, interactive behavior generation through symbol manipulation, and VR for data collection. The talk emphasizes long-term human capability enhancement and avoiding over-reliance on technology. Why it matters: This research promotes independence and growth in human-robot interactions, potentially revolutionizing assistive technologies in the region.
Eduardo da Veiga Beltrame, bioinformatics lead at ImYoo (a Caltech spinout), presented on scalable methods for single-cell omics data analysis, including kallisto|bustools and scvi-tools. He highlighted their use in ImYoo's decentralized longitudinal study on Inflammatory Bowel Disease (IBD), where patients self-collect capillary blood samples. Beltrame also discussed his research on STEM education programs in Brazil as a visiting scholar at UC Berkeley. Why it matters: This highlights the growing trend of decentralized clinical studies leveraging advanced single-cell technologies for precision medicine, showcasing the potential of remote data collection and analysis in understanding complex diseases.
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.
IBM Fellow Dr. Tanveer Syeda-Mahmood gave a talk on the evolution of foundational models, covering multimodal fusion in healthcare and neuro-inspired AI for computer vision. She also discussed image-driven fact-checking of generative AI textual reports for responsible models. Dr. Syeda-Mahmood leads IBM's work in Multimodal Bioinspired AI and WatsonX features, and previously led the Medical Sieve Radiology Grand Challenge. Why it matters: The talk highlights the ongoing development and application of AI foundational models in critical areas like healthcare and responsible AI development, showing IBM's continued investment in these areas.
Natasa Przulj at the Barcelona Supercomputing Center is developing an AI framework that fuses multi-omic data to improve precision medicine. The framework uses graph-regularized non-negative matrix tri-factorization (NMTF) and network science algorithms for patient stratification, biomarker prediction, and drug repurposing. It is applied to diseases like cancer, Covid-19, and Parkinson's. Why it matters: This research can enable more personalized and effective treatments by leveraging complex biological data to understand disease mechanisms and tailor therapies.
Researchers are exploring methods for evaluating the outcome of actions using off-policy observations where the context is noisy or anonymized. They employ proxy causal learning, using two noisy views of the context to recover the average causal effect of an action without explicitly modeling the hidden context. The implementation uses learned neural net representations for both action and context, and demonstrates outperformance compared to an autoencoder-based alternative. Why it matters: This research addresses a key challenge in applying AI in real-world scenarios where data privacy or bandwidth limitations necessitate working with noisy or anonymized data.
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.
Pascal Fua from EPFL presented an approach to implementing convolutional neural nets that output complex 3D surface meshes. The method overcomes limitations in converting implicit representations to explicit surface representations. Applications include single view reconstruction, physically-driven shape optimization, and bio-medical image segmentation. Why it matters: This research advances geometric deep learning by enabling end-to-end trainable models for 3D surface mesh generation, with potential impact on various applications in computer vision and biomedical imaging in the region.
Paul Liang from CMU presented on machine learning foundations for multisensory AI, discussing a theoretical framework for modality interactions. The talk covered cross-modal attention and multimodal transformer architectures, and applications in mental health, pathology, and robotics. Liang's research aims to enable AI systems to integrate and learn from diverse real-world sensory modalities. Why it matters: This highlights the growing importance of multimodal AI research and its potential for advancements across various sectors in the region, including healthcare and robotics.
Sai Praneeth Karimireddy from UC Berkeley presented a talk on building planetary-scale collaborative intelligence, highlighting the challenges of using distributed data in machine learning due to data silos and ethical-legal restrictions. He proposed collaborative systems like federated learning as a solution to bring together distributed data while respecting privacy. The talk addressed the need for efficiency, reliability, and management of divergent goals in these systems, suggesting the use of tools from optimization, statistics, and economics. Why it matters: Collaborative AI systems can unlock valuable distributed data in the region, especially in sensitive sectors like healthcare, while ensuring privacy and addressing ethical concerns.
Pietro Liò from the University of Cambridge will discuss geometric deep learning techniques for building a digital patient twin using graph and hypergraph representation learning. The talk will focus on integrating Computational Biology and Deep Learning, considering physiological, clinical, and molecular variables. He will also cover explainable methodologies for clinicians and protein design using diffusion models. Why it matters: This highlights the growing interest in applying advanced AI techniques like geometric deep learning and diffusion models to healthcare challenges in the region, particularly for personalized medicine.
The article discusses the potential of AI-enabled assistive technologies to empower People with Disabilities (PWD), citing that over one billion people live with some form of disability globally. It highlights examples like communication tools, assistive robots, and smart visual aids, and emphasizes the need to address security and privacy concerns. The author, Ishfaq Ahmad from the University of Texas at Arlington, points out that with a growing global population, over two billion people will need assistive products by 2030. Why it matters: The piece advocates for using AI to tackle critical human rights issues and improve the lives of a significant portion of the global population in the face of increasing disability rates.
Daisuke Kihara from Purdue University presented a seminar at MBZUAI on using deep learning for biomolecular structure modeling. His lab is developing 3D structure modeling methods, especially for cryo-electron microscopy (cryo-EM) data. They are also working on RNA structure prediction and peptide docking using deep neural networks inspired by AlphaFold2. Why it matters: Applying advanced deep learning techniques to biomolecular structure prediction can accelerate drug discovery and our understanding of molecular functions.
MBZUAI alumnus Ahmed Sharshar is developing smaller AI models to make the technology more accessible, especially in resource-constrained environments like Egypt. His master's thesis involved creating an app that assesses lung health using mobile phone video analysis, eliminating the need for traditional medical devices. Sharshar is pursuing his Ph.D. at MBZUAI, focusing on lightweight and energy-efficient models for various applications. Why it matters: Democratizing AI through smaller, efficient models can enable broader applications and innovation across diverse sectors in the Middle East and beyond.
A panel discussion was hosted at MBZUAI in collaboration with the Manara Center for Coexistence and Dialogue. The discussion centered on the potential of multimodal machine intelligence for human-centered applications, particularly in health and wellbeing. USC Professor Shrikanth Narayanan spoke on creating trustworthy and inclusive AI that considers protected variables. Why it matters: This signals MBZUAI's interest in exploring ethical AI development and its applications for societal good, potentially driving research and policy initiatives in the region.
MBZUAI's Incubation and Entrepreneurship Center (MIEC), launched in November 2023, is fostering AI-driven startups, including LibrAI, Audiomatic, and Limb. LibrAI is an AI safety monitoring platform founded by MBZUAI postdoctoral researcher Xudong Han. Audiomatic, created by MBZUAI students Muhammad Taimoor Haseeb and Ahmad Hammoudeh, is an AI-powered audio integration platform. Why it matters: These startups demonstrate MBZUAI's role in translating AI research into practical solutions, contributing to the UAE's innovation ecosystem and addressing real-world challenges.
MBZUAI alumna Akbobek Abilkaiyrkyzy, who graduated with a master's in machine learning in 2022, has been involved in various domains, including industry, entrepreneurship, and sustainability. Her master's thesis focused on developing a chatbot for mental health problem detection, leading to the creation of WellRound, an app that uses data from various sources to improve mental and physical wellbeing. She further developed the app with support from the MBZUAI Incubation and Entrepreneurship Center (MIEC). Why it matters: This highlights MBZUAI's role in fostering AI innovation and entrepreneurship in the healthcare sector, as well as empowering its graduates to create solutions addressing critical societal needs.
MBZUAI researchers presented a new causal discovery method at NeurIPS that identifies relationships between deterministic and non-deterministic variables. The method builds directed graphs visualizing relationships between variables, incorporating both probabilistic and deterministic principles. The lead author, Longkang Li, aims to apply causal discovery to healthcare and biology for better understanding of diseases. Why it matters: This research advances the field of causal inference, potentially improving applications in areas like healthcare where understanding complex relationships is critical.
Researchers from MBZUAI and Monash University presented a study at EMNLP 2024 examining LLMs' ability to interpret empathy, emotion, and morality in written stories. The study builds on a framework for modeling empathic similarity between narratives, using the EmpathicStories dataset. They are exploring ways to improve LLMs' capabilities with complex concepts like empathy, especially for applications in fields like healthcare. Why it matters: Enhancing LLMs with empathic understanding could lead to more effective and human-centered AI applications, particularly in sensitive domains requiring nuanced communication.
MBZUAI visiting professor of computer vision, Xiaojun Chang, has been named to Clarivate’s 2024 Highly Cited Researchers list, placing him in the top 1% of researchers worldwide. Chang has accumulated over 18,000 citations from around 200 papers, with his research focusing on multimodal foundation models and their applications to embodied AI and healthcare. Chang also holds positions at the University of Technology Sydney and RMIT University in Australia. Why it matters: This recognition highlights MBZUAI's growing impact and its role in fostering interdisciplinary collaboration and innovation in AI research, attracting further opportunities and collaborations.
Nutrigenics, an AI startup founded by MBZUAI PhD students, is developing a platform to improve dietetics. The platform features 200,000 tagged recipes and a vision language model (VLM) that analyzes meal photos with 70% accuracy. Nutrigenics aims to bridge the gap between clinical advice and daily eating habits, enabling dieticians to monitor patient progress more effectively. Why it matters: This technology can personalize nutrition at scale in the region and improve adherence to dietary guidelines, addressing a critical need in preventative healthcare.
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.
Freddy Jimenez, regional innovation lead for Pfizer in the Middle East, Africa, and Russia, is working to implement AI solutions for healthcare challenges in the UAE. Jimenez joined the Executive Education Program at MBZUAI to expand his knowledge and drive the country's healthcare industry forward. He emphasizes the importance of strategic approaches to AI in healthcare due to rapid technological advancements outpacing regulation. Why it matters: This highlights the UAE's focus on leveraging AI to improve healthcare outcomes and the importance of collaboration between industry, academia, and government to accelerate AI adoption in the heavily regulated healthcare sector.
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's Eduardo da Veiga Beltrame is developing machine learning tools for analyzing single-cell RNA sequencing data, which measures RNA in thousands of individual cells. Sequencing costs have decreased faster than Moore's Law, enabling large-scale data collection in biology. RNA sequencing provides insights into gene expression and cellular activity, crucial for personalized medicine. Why it matters: Advancements in single-cell RNA sequencing and ML analysis will accelerate personalized medicine by providing detailed insights into cellular mechanisms and disease pathways.
The AI4Bio Workshop at MBZUAI explored the intersection of AI and biology, focusing on AI-driven virtual organisms and foundation models. Eric Xing presented his vision of using AI to simulate biological activities, offering a safer alternative to physical experiments. Researchers like Le Song and Jen Philippe Vert are developing foundation models for biological systems, enhancing drug discovery and bioengineering. Why it matters: This signals the growing importance of AI in advancing biological research and healthcare innovation within the UAE and globally.
MBZUAI M.Sc. graduate Shamma Al Saedi won the Ministry of Education Entrepreneurship Challenge prize for her startup, Limb, an AI-powered physiotherapy system. The Limb app uses a personalized 3D model and pose estimation to correct patient posture and provide feedback, while the Limb API allows physiotherapists to monitor patient progress. Al Saedi credits MBZUAI's Incubation and Entrepreneurship Center (IEC) for providing knowledge, access to talks, and networking opportunities that helped her develop the startup. Why it matters: This highlights the potential of AI and entrepreneurship programs within UAE universities to foster innovative healthcare solutions and support local talent.
MBZUAI alumnus Adnan Khan is pursuing a Ph.D. at Carleton University, focusing his research on using computer vision to improve accessibility in healthcare, particularly for the visually impaired. His work builds upon his master's thesis at MBZUAI, which focused on domain generalization, enabling models to adapt across different data domains. Khan credits his experiences at MBZUAI for shaping his community spirit and career path. Why it matters: This highlights the role of AI education in fostering socially impactful research and driving innovation in healthcare accessibility in the region and beyond.
Researchers from MBZUAI and other institutions presented a study at ACL 2024 on combatting misinformation by identifying misrepresented scientific research. They compiled a dataset called MISSCI, comprised of real-world examples of misinformation gathered from the HealthFeedback fact-checking website. The annotators classified the different types of errors in reasoning into nine different classes. Why it matters: This work addresses a critical need to combat the spread of scientific falsehoods online, especially given the challenges of manual fact-checking.
MBZUAI's first Ph.D. graduate, Numan Saeed, developed deep learning models to diagnose head and neck cancers using PET and CT scan imagery. His research focused on improving early detection and accurate localization of tumors, aiming to enhance diagnosis and prognosis. Early diagnosis can reduce mortality rates by up to 70%. Why it matters: This research showcases the potential of AI in healthcare to improve cancer diagnosis and treatment, addressing a critical need in resource-constrained healthcare systems.
MBZUAI graduate Ahmed Sharshar developed a computer vision application that assesses lung health from a video of a person breathing, estimating Forced Vital Capacity (FVC), Forced Expiratory Volume in 1 second (FEV1), and Peak Expiratory Flow (PEF). The model achieved up to 100% accuracy using thermal video data from 60 participants. Sharshar aims to create lightweight models applicable in developing countries without high-end GPUs. Why it matters: This research showcases the potential of AI to democratize healthcare access through non-invasive, accessible diagnostic tools.
MBZUAI Ph.D. graduate Hilal Mohammad Hilal AlQuabeh researched methods to improve the efficiency of machine learning algorithms, specifically focusing on pairwise learning and multi-instance learning. Pairwise learning teaches AI to make decisions by comparing options in pairs, useful for ranking and anomaly detection. Multi-instance learning involves learning from sets of data points, applicable in areas like drug discovery. Why it matters: Optimizing AI for low-resource environments expands its accessibility and applicability in critical sectors like healthcare and remote area operations.
MBZUAI will graduate its first three Ph.D. students on June 6, 2024. Numan Saeed's research focused on using AI and big data to diagnose head and neck cancers using PET and CT scans, aided by NLP to interpret doctors' notes. The graduates will remain in the UAE to contribute to the country's growing AI sector. Why it matters: This milestone highlights the growing AI talent pool in the UAE and the potential for AI research at MBZUAI to address critical healthcare challenges.
The article discusses how AI is transforming healthcare, aviation, and agriculture. MBZUAI has launched the Institute of Digital Public Health (IDHP) and partnered with the Department of Health – Abu Dhabi and Core42 to launch the Global AI Healthcare Academy. MBZUAI has also partnered with Etihad Airways to explore AI applications in aviation and with Silal to bring AI innovation to agriculture and food production. Why it matters: These initiatives signal the UAE's commitment to becoming a global AI hub by integrating AI across key sectors and fostering innovation through strategic partnerships.
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 is highlighting five female leaders in AI for International Women’s Day, noting its 28% female student body. Dr. Farida Al Hosani is developing an AI healthcare solution for non-communicable diseases and was appointed VP of MBZUAI’s Alumni Advisory Board. Dr. Hanan Aldarmaki focuses on improving Arabic automated speech recognition and recently won an award for a paper on Arabic speech processing. Why it matters: Showcasing women in AI leadership helps promote diversity and inclusion in the field, especially in the context of the rapidly growing AI ecosystem in the UAE.
MBZUAI and the Weizmann Institute of Science (WIS) jointly hosted the inaugural Human Phenotype Project Hackathon in Abu Dhabi, focusing on AI-driven healthcare solutions. 24 postgraduate students and researchers analyzed "real data" from a deep-phenotype multi-omic biobank to develop personalized and predictive analytics. The hackathon utilized data from WIS and Pheno.AI’s Human Phenotype Project (HPP) in Israel, marking the first time an international university has been granted access to this data. Why it matters: This collaboration demonstrates the growing emphasis on leveraging AI for healthcare innovation in the UAE and fostering international partnerships to address global health challenges.
MBZUAI Professor Kun Zhang is working on applying AI to understand cause-and-effect relationships in biology, with the goal of accelerating scientific discovery and improving human health. He aims to develop foundation models for biology that can process diverse data types and provide insights into the causes and treatments of health problems. These models could help scientists develop new medicines and preventative measures for diseases. Why it matters: This research has the potential to significantly advance the field of medicine by enabling a deeper understanding of the complex biological processes that underlie disease.
MBZUAI's AI Quorum workshop featured Yale biostatistics professor Heping Zhang discussing the challenges of using AI and statistics to analyze noisy biological data for health insights. Zhang highlighted the need to develop methods to extract meaningful stories from noisy data to understand brain function and genetic roles in disease regulation. Harvard's Xihong Lin presented recommendations for building an ecosystem using AI and statistics to improve understanding of the relationship between genome sequences and biological functions. Why it matters: This discussion underscores the importance of AI and statistical methods in addressing the complexities of biological data, particularly in understanding neurological diseases like Alzheimer's, and highlights the need for centralized data infrastructure.
MBZUAI is hosting an "AI Quorum on Statistics for the Future of AI" in Abu Dhabi, focusing on the intersection of statistics and AI in healthcare. Organized by Professors Tian Zheng (Columbia University) and Hongtu Zhu (UNC), the event gathers experts from top global universities and organizations like Eli Lilly and MD Anderson Cancer Center. The workshop aims to integrate statistical insights into AI research, fostering innovations in the field. Why it matters: By convening international experts, MBZUAI is positioning itself as a hub for interdisciplinary AI research with a focus on healthcare applications.
MBZUAI hosted the Second Workshop on Collaborative Learning as part of the AI Quorum in Abu Dhabi, focusing on collaborative and federated learning for sustainable development. Researchers discussed applications in medicine, biology, ecological conservation, and humanitarian aid. Eric Xing highlighted the potential of large biology models, similar to LLMs, to revolutionize biological data analysis. Why it matters: This workshop underscores the UAE's commitment to advancing AI research in crucial sectors like healthcare and sustainability through collaborative learning approaches.
MBZUAI and United Al-Saqer Group have signed a 10-year research endowment agreement to advance AI in digital health. The funds will support health research projects at MBZUAI under the name of H.E. Sheikh Mohamed bin Butti Al Hamed. MBZUAI is already conducting research projects in areas like malaria spread, early detection of cardio and fetal abnormalities, and remote patient monitoring. Why it matters: This partnership highlights the UAE's commitment to leveraging AI for healthcare innovation and aligns with Abu Dhabi's 2030 Economic Vision.
MBZUAI held an appreciation ceremony recognizing over 30 industry partners for their collaboration in building the AI ecosystem in Abu Dhabi. The event included panel discussions on AI's potential in healthcare and sustainability, featuring speakers from Mubadala Healthcare, Sheikh Shakhbout Medical City, and the Department of Health – Abu Dhabi. Faculty discussed advancements in NLP and MBZUAI's role in developing Jais and Jais Climate, along with progress in computer vision and machine learning. Why it matters: This event highlights MBZUAI's commitment to fostering partnerships that advance AI research and applications, particularly in areas crucial to the UAE's strategic priorities like healthcare and climate change.
Cleveland Clinic Abu Dhabi and MBZUAI have signed an MoU to collaborate on research and education initiatives. The partnership aims to create opportunities such as joint research projects and mentorship programs for students. Both institutions will leverage their expertise to explore AI applications in healthcare, focusing on areas like diagnostics and digital twins. Why it matters: The collaboration will strengthen Abu Dhabi's position as a hub for AI-enabled healthcare innovation and talent development.
MBZUAI awarded its first Ph.D. to Numan Saeed for his dissertation, "Deep Learning for Cancer Diagnosis and Prognosis." Saeed's research explores using deep learning models to diagnose tumors and predict survival rates for head and neck cancers by interpreting PET and CT scan imagery and doctor's notes. The AI model considers variables like age, gender, and tumor size to improve diagnosis, especially in regions with limited oncological services. Why it matters: This milestone highlights MBZUAI's growing role in AI research and its potential to contribute to advancements in AI-driven healthcare solutions, particularly for cancer diagnosis and treatment.
MBZUAI students won the top three awards at the Alibaba AI Hackathon in Dubai, part of GITEX Global AI InnovateFest. First place went to TalkTrain, a smart business assistant for public speaking practice, developed by Kane Lindsay and Hawau Olamide Toyin. Second place was awarded to Anaaya, a generative AI application for predicting adverse drug interactions, created by Mai A. Shaaban and Anees Ur Rehman Hashmi. Why it matters: This sweep highlights MBZUAI's strength in applied AI research and the potential for its students to create impactful solutions for real-world problems.
MBZUAI Professor Fakherddine Karray is developing deep learning algorithms for human activity recognition to monitor the health and safety of elderly people. The AI tools analyze movement, posture, and facial expressions to detect early warning signs of health emergencies. Remote patient monitoring systems integrate smart devices and secure communication to allow elderly patients to stay at home and communicate with healthcare providers. Why it matters: AI-powered smart homes can provide affordable healthcare solutions for the rapidly growing elderly population in the region and worldwide.