AI applications in healthcare across the GCC — including medical imaging, clinical decision support, genomics, and health informatics from Gulf universities and hospitals.
KAUST professors Samir Hamdan and Nina Fedoroff collaborated on research published in Nucleic Acids Research focusing on microRNA (miRNA) biogenesis in plants. The study examined miRNA production in Arabidopsis thaliana and found that the protein SERRATE (SE) is integral to the processing of pri-miRNA by DCL1. They characterized the interactions of SE with RNA and DCL1, elucidating the mechanism by which SE promotes DCL1 activity. Why it matters: Understanding miRNA biogenesis could help modify crop plants to better tolerate stressful conditions, potentially increasing crop yields and productivity in the region.
MBZUAI alumnus Numan Saeed is applying machine learning to medical imaging and cancer research as a research scientist at the University. He collaborates with UAE hospitals like Sheikh Shakhbout Medical City and Cleveland Clinic to build datasets and gather clinical feedback. Saeed's team is developing a model focused on head and neck cancer using the HECKTOR dataset and local data. Why it matters: This research contributes to the UAE's healthcare ambitions by enabling earlier diagnosis and personalized care through AI-driven analysis of medical images.
MBZUAI Visiting Professor Haiyan Huang is working on bridging biology and AI by incorporating domain knowledge into modeling frameworks. She combines statistical principles, AI tools, and domain expertise to develop scientifically informed and statistically grounded methods. Her work addresses the challenge of extracting meaningful signals from complex biological data. Why it matters: This interdisciplinary approach can lead to more accurate and useful AI models for biological research and healthcare applications in the region.
The Special Olympics Global Center Summit in Abu Dhabi convened 300 advocates to discuss social inclusion for individuals with intellectual disabilities. A panel including MBZUAI's Elizabeth Churchill highlighted AI's role in inclusive technology design, especially in education. Churchill noted AI can personalize learning through tailored regimens, emotion detection, and understanding cognitive patterns. Why it matters: AI-driven personalization has potential to transform education and accessibility for children of determination and other underrepresented groups in the region.
MBZUAI will present two assistive AI prototypes at GITEX 2025: smart glasses with a camera and eye tracker that identify objects and medication, and a brain-computer interface (BCI) device integrated with robotics to control a robotic dog's movements. The smart glasses use a multimodal large language model (LLM) to help visually impaired individuals, while the BCI aims to restore hands-free communication for people with mobility limitations. Hisham Cholakkal leads the research team, which received a Meta Regional Research Grant 2025 for its work on multimodal LLM for smart wearables. Why it matters: The research demonstrates the potential of AI to improve the quality of life for vulnerable populations and addresses the challenge of providing cost-effective care for aging societies.
MBZUAI hosted AI Innovation Day, uniting over 200 UAE leaders from healthcare, government, science, and research to foster collaborations in AI-driven healthcare. The event featured sessions on multimodal foundation models, human genomics, longevity science, and robotics-enabled therapeutics. UAE's Minister of Health and Prevention, H.E. Ahmed bin Ali Al Sayegh, emphasized AI's fundamental role in the future of healthcare in the UAE. Why it matters: The event highlights the UAE's commitment to becoming a global hub for digital healthcare research and innovation, positioning MBZUAI as a key driver in the region's healthcare ecosystem.
MBZUAI researchers developed LetBabyTalk, an AI-powered multilingual parenting app that analyzes baby cries to identify needs like hunger or sleepiness. The app is trained on over 1,000 baby cries and uses supervised machine learning with input from experienced parents and educators. Cradle AI, the startup behind the app, aims to bridge the gap between advanced AI research and real-world solutions, focusing on family care and education. Why it matters: This project demonstrates the potential of AI to address everyday challenges and improve the lives of families in the region and globally, while also showcasing MBZUAI's focus on AI for social good.
MBZUAI's Assistant Professor of Computational Biology, Eduardo Beltrame, is researching single-cell RNA sequencing to advance personalized medicine. He is also designing MBZUAI’s new master’s and Ph.D. programs in computational biology, set to launch in 2026, under the guidance of Professor Eran Segal. MBZUAI's research agenda includes foundational initiatives like AIDO and the Human Phenotype Project, leveraging vast datasets such as the Emirati Genome Project. Why it matters: This highlights MBZUAI's commitment to cutting-edge research and education in computational biology, positioning it as a potential rival to top global institutions in the field.
Dr. Pooja Khosla, formerly an academic, co-founded Entelligent, a climate fintech startup that uses big data and machine learning to help companies manage climate change risk. Khosla recently spoke at MBZUAI, advising academics to translate research into real-world applications, leveraging their unique access to data and analytical thinking. She emphasized the importance of simplifying complex work to make it accessible, noting AI's role in accelerating complex tasks. Why it matters: This highlights the growing trend of translating academic research into practical, impactful business solutions within the AI and climate tech sectors, potentially inspiring more researchers in the region to pursue entrepreneurial ventures.
Sir Michael Brady, professor at Oxford and MBZUAI, argues that AI in healthcare must move beyond pattern recognition to causal understanding. He states that clinicians require AI models to articulate their reasoning behind diagnoses and therapy recommendations, not just provide statistical scores. He believes AI's immediate impact will be in personalized medicine, tailoring treatments to the individual rather than relying on epidemiological averages. Why it matters: This perspective highlights the critical need for explainable AI in sensitive domains like healthcare, paving the way for more trustworthy and clinically relevant AI applications in the region.
Professor Cesare Stefanini of MBZUAI discusses his journey from Pisa to global prominence in robotics. He credits his time at Scuola Superiore Sant’Anna and mentorship from Paolo Dario for shaping his career. Stefanini won an international robotics competition in Japan, which inspired him to pursue a Ph.D. and further research. Why it matters: This highlights MBZUAI's commitment to attracting top international talent in robotics and fostering innovation in healthcare technology.
The inaugural Abu Dhabi AI-Robotics Conference was held at MBZUAI, focusing on AI-powered robotics to transform healthcare. Experts explored applications in microsurgery, biorobotics, and personalized treatment, with keynotes from H.E. Abdulla Abdulalee AlHumaidan, Timothy Baldwin, and Sami Haddadin. Dr. Hassa Al Mazrouei highlighted the potential for personalized care and automation driven by AI. Why it matters: The conference underscores the UAE's commitment to advancing AI and robotics in healthcare, potentially positioning the region as a leader in innovative medical technologies.
Olivier Oullier, Visiting Professor at MBZUAI, is working on brain-computer interfaces, founding Inclusive Brains to develop a Neural Foundation Model using neurophysiological and behavioral signals. This model integrates data from brainwaves, eye-tracking, and other modalities to allow machines to build a representation of the world closer to human cognition. Why it matters: Such advancements can transform human-computer interaction, with particular implications for people of determination in the region.
MBZUAI is developing AI-powered applications to help reduce malaria's impact in Indonesia, supported by Sheikh Mohamed bin Zayed Al Nahyan's Reaching the Last Mile initiative. The applications use sensory data fusion to create "digital twins" for precise weather forecasting and real-time environmental representation. AI and clustering analysis identify recurring features contributing to malaria outbreaks, enabling preventative measures and early treatment. Why it matters: This project demonstrates AI's potential in combating climate-sensitive diseases and improving public health in vulnerable regions.
Malaria No More (MNM), Reaching the Last Mile (RLM), and MBZUAI have signed an agreement to expand the Forecasting Healthy Futures (FHF) initiative with a $5 million award from RLM. The initiative aims to address the impact of climate change on malaria and other climate-sensitive infectious diseases. MBZUAI will provide expertise to support the eradication of malaria. Why it matters: This partnership highlights the UAE's commitment to global health and leverages AI to combat climate-sensitive diseases, demonstrating a proactive approach to addressing complex global challenges.
MBZUAI alumnus Ikboljon Sobirov is using AI to develop new diagnostic tools for cardiovascular disease at the University of Oxford. His research focuses on building imaging biomarkers by integrating transcriptomic data with medical scans. The goal is to predict how a patient will respond to specific medications using only images. Why it matters: This work showcases the potential of AI and multi-modal data to personalize medicine and improve healthcare outcomes in the region and globally.
MBZUAI Associate Professor Mohammad Yaqub is focused on translating AI research into real-world healthcare solutions. His previous work includes the development of SanNav, an AI-based fetal anomaly detection system that became an FDA-approved product used by GE Healthcare and used on his own wife during pregnancy. Yaqub joined MBZUAI to help build a new model of AI research and education with a focus on interdisciplinary collaboration and industry partnerships. Why it matters: This highlights the UAE's growing focus on AI in healthcare and MBZUAI's role in bridging the gap between research and practical applications in the medical field.
MBZUAI alumnus Kane Lindsay is using AI to digitize and analyze plant specimens at Kew Gardens, including handwritten notes and preserved plants dating back centuries. He developed an AI model to recognize handwriting (OCR) and extract phenotype data from plant images, and another to redact sensitive location data. This work began as an internship and evolved into a fully funded Ph.D. Why it matters: AI is accelerating the digitization and preservation of plant collections, making them more accessible to researchers and aiding in conservation efforts.
MBZUAI doctoral student Umaima Rahman is researching domain adaptation and generalization in deep learning for medical imaging to improve AI model performance across diverse hospitals and equipment. Her work focuses on building models that learn consistent features across different data sources to ensure reliability in various healthcare settings. Rahman emphasizes that generalization in healthcare AI is a necessity, especially in resource-limited settings, and aims to develop AI that assists clinicians rather than replaces them. Why it matters: This research addresses a critical challenge in deploying AI in healthcare, ensuring that models can be reliably used in diverse settings, particularly benefiting developing countries and improving global healthcare accessibility.
MBZUAI doctoral student Hawau Toyin is applying AI to the identification, correction, and evaluation of stuttering, particularly in developing countries where it often goes undiagnosed. She is collaborating with the SpeechCare Center UAE and her advisor Dr. Hanan Aldarmaki to develop AI tools for faster and more accessible diagnosis and treatment. The research focuses on data collection from around the world to build an effective AI system that can analyze the various forms of stuttering. Why it matters: This research addresses a critical healthcare gap by leveraging AI to improve diagnosis and treatment of speech disorders in underserved regions.
MBZUAI valedictorian Salma Hassan developed a multimodal graph learning approach for early dementia diagnosis and prognosis. Her master's thesis focused on differential diagnosis to identify specific dementia subtypes and predicting cognitive decline in preclinical patients. The AI model leverages brain imaging, genetics, and clinical records to improve diagnostic accuracy. Why it matters: This research can improve early detection and resource allocation for dementia management, especially in developing countries.
MBZUAI researchers presented a new causal representation learning framework at ICLR for identifying latent causal variables in multimodal biological data. The framework addresses the challenge of uncovering underlying causal factors from lab tests, genetic information, and medical images. The new approach can identify latent causal variables and their influence on observed biological outcomes across modalities. Why it matters: The model's ability to analyze causal mechanisms between modalities can lead to more complete insights in biomedical research.
MBZUAI is developing AI technologies to improve understanding and conservation of marine environments. AI algorithms analyze data from satellite imagery, ROVs, and sensors to identify patterns and trends, with machine learning models predicting oceanographic phenomena. Computer vision automates the identification of marine organisms, aiding in biodiversity assessments and ecosystem health evaluations. Why it matters: This research supports sustainability goals, such as improving biodiversity assessments and enabling faster responses to environmental events in the region's sensitive marine ecosystems.
MBZUAI hosted a Single Cell Summer Workshop, gathering over 25 participants and speakers to explore single-cell omics data. The two-day event featured three workshops, nine talks, and networking opportunities, aiming to develop bioinformatics skills and strengthen the regional ecosystem. Workshops were led by Eduardo Beltrame (MBZUAI), Mariano Gabitto (Allen Institute), and Luke Zappia (Data Intuitive), covering topics like scvi-tools, multimodal machine learning, and benchmarking. Why it matters: This workshop signifies MBZUAI's commitment to advancing bioinformatics research in the UAE and fostering collaboration within the regional scientific community.
MBZUAI's Computational Biology department hosted the first Data Carpentry workshop in the UAE, a global initiative focused on foundational data science skills for researchers. The workshop included 35 participants from academia and industry, covering topics like data tidiness, cloud computing, command lines, and data wrangling. 70% of the participants were female, and several were Emirati nationals. Why it matters: This initiative addresses the growing need for computational skills in genomics research, bridging the gap between wet lab experiments and data analysis for scientists in the UAE.
Maryam Ahmed Hassani, an MBZUAI graduate and head of trends and innovation at Abu Dhabi’s Early Childhood Authority (ECA), is integrating AI into early childhood development to create efficient and human-focused systems. She aims to develop tools that personalize support for parents and predict developmental risks early on. Hassani also co-founded Zealous, an AI startup focused on AI code generation and testing, launching AI-native products including an AI-automated tester for websites and web apps. Why it matters: This highlights the growing application of AI in public sector challenges within the UAE, specifically in early childhood development and software development.
MBZUAI will participate as an official partner at Machines Can See 2025 (MCS 2025) during Dubai AI Week, showcasing AI innovation with expert speakers and live demos. MBZUAI will present BiMediX2, a multimodal virtual assistant that answers medical questions in Arabic and English and won Meta’s Llama Impact Innovation Award. They will also demonstrate Voodoo XP, a one-shot face re-enactment tool, and LAIKA, an AI-powered robot dog. Why it matters: This event highlights MBZUAI's contributions to AI and robotics, strengthening the UAE's position as a leader in science and technology, especially in healthcare and advanced robotics.
Dr. Farida Al Hosani, a UAE public health leader and MBZUAI Executive Program graduate, has been at the forefront of the nation’s public health strategy, specializing in infectious diseases. She emphasizes the growing importance of AI in healthcare, particularly in areas like early detection, drug discovery, and public health strategies. Al Hosani has worked on several AI initiatives, including building algorithms and predictive modeling systems to bridge gaps in human health, especially in prevention. Why it matters: This highlights the increasing role of AI in transforming healthcare in the UAE, with public health leaders leveraging AI tools to improve disease prevention and treatment.
MBZUAI graduate Svetlana Maslenkova worked with Assistant Professor Mohammad Yaqub on a project focused on the earlier detection of kidney failure using tabular data. Maslenkova's master's thesis involved predicting Acute Kidney Injury (AKI) using Electronic Health Records (EHR), specifically the MIMIC-IV v2.0 database. She found that patient weight distribution was a factor in the severity of kidney failure. Why it matters: This research highlights the potential of AI and machine learning to improve healthcare outcomes through the analysis of often-overlooked tabular data in electronic health records.
Faris Almalik, an MBZUAI alumnus, discusses his journey from mechanical engineering to becoming a senior data scientist at the Department of Government Enablement – TAMM in Abu Dhabi. Almalik emphasizes his passion for continuous learning and knowledge sharing, which has driven his career across defense, education, finance, and government sectors. He published his first paper on adversarial attacks on medical imaging at MICCAI during his time at MBZUAI. Why it matters: This highlights the success of MBZUAI in producing impactful AI professionals who contribute to both research and practical applications in key sectors within the UAE.
MBZUAI is hosting the Augmented Humans International Conference 2025 in the Middle East for the first time, in cooperation with the ACM. The conference focuses on physical, cognitive, and perceptual augmentations of humans through digital technologies. 30 papers were selected from 77 submissions, with participation from researchers from institutions like MIT Media Lab and the University of Tokyo. Why it matters: This event underscores the UAE's commitment to AI research and human-computer interaction, fostering collaboration and driving progress in augmentation technologies for societal benefit.
A talk discusses the challenges of single-cell data analysis, such as feature sparsity and the effects of rare cells. AI/ML strategies are uniquely positioned to model this data. ImYoo, a startup founded in 2021, is applying single-cell model architectures for unsupervised discovery of patient groupings and predicting sample-level phenotypical data in autoimmune disease. Why it matters: This highlights the growing application of AI/ML in analyzing single-cell data for population-scale human health studies, an area ripe for innovation and improvement in the Middle East's growing biotech sector.
Researchers at the Rosalind Franklin Institute are using generative AI, including GANs, to augment limited biological datasets, specifically mirtron data from mirtronDB. The synthetic data created mimics real-world samples, facilitating more comprehensive training of machine learning models, leading to improved mirtron identification tools. They also plan to apply Large Language Models (LLMs) to predict unknown patterns in sequence and structure biology problems. Why it matters: This research explores AI techniques to tackle data scarcity in biological research, potentially accelerating discoveries in noncoding RNA and transposable elements.
The Russian Immune Diversity Atlas project aims to profile immune cells from people of different ancestries at a multiomics level. The goal is to reconstruct a reference atlas of the healthy immune system and investigate its perturbations in Type II Diabetes (T2D). The project seeks to identify novel mechanisms and genetic/epigenetic markers for early T2D diagnostics, prognosis, and therapy as part of the international Human Cell Atlas. Why it matters: Addressing genetic diversity in biomedical research, particularly in the context of the Human Cell Atlas, is crucial for personalized medicine and ensuring that treatments are effective across diverse populations in the Middle East and globally.
Søren Brunak presented deep learning approaches for analyzing disease trajectories using data from 7-10 million patients in Denmark and the USA. The models predict future outcomes like mortality and specific diagnoses, such as pancreatic cancer, using 15-40 years of patient data. Disease trajectories and explainable AI can generate hypotheses for molecular-level investigations into causal aspects of disease progression. Why it matters: This research demonstrates the potential of large-scale patient data and AI to improve disease prediction and generate hypotheses for further investigation into disease mechanisms relevant to regional healthcare systems.
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.
Dr. Mikhail Burtsev of the London Institute presented research on GENA-LM, a suite of transformer-based DNA language models. The talk addressed the challenge of scaling transformers for genomic sequences, proposing recurrent memory augmentation to handle long input sequences efficiently. This approach improves language modeling performance and holds promise for memory-intensive applications in bioinformatics. Why it matters: This research can significantly advance AI's capabilities in genomics by enabling the processing of much larger DNA sequences, with potential breakthroughs in understanding and treating diseases.
Xiao Wang from Purdue University presented research on Adversarial Contrastive Learning (AdCo) and Cooperative-adversarial Contrastive Learning (CaCo) for improved self-supervised learning. He also discussed CryoREAD, a framework for building DNA/RNA structures from cryo-EM maps, and future work in deep learning for drug discovery. Wang's algorithms have impacted molecular biology, leading to new structure discoveries published in journals like Cell and Nature Microbiology. Why it matters: The research advances AI techniques for crucial tasks in molecular biology and drug discovery, with potential applications for institutions in the GCC region focused on healthcare and biotechnology.
A talk will present two projects related to the use of NLP for estimating a client’s depression severity and well-being. The first project examines emotional coherence between the subjective experience of emotions and emotion expression in therapy using transformer-based emotion recognition models. The second project proposes a semantic pipeline to study depression severity in individuals based on their social media posts by exploring different aggregation methods to answer one of four Beck Depression Inventory (BDI) options per symptom. Why it matters: This research explores how NLP techniques can be applied to mental health assessment, potentially offering new tools for diagnosis and treatment monitoring.
This article discusses the use of artificial intelligence in precision oncology, particularly in understanding individual tumor mechanisms and aiding clinical decision-making. Dr. Xinghua Lu, with extensive experience in medicine and biomedical informatics, will present research on individualized Bayesian causal inference methods for investigating oncogenic mechanisms. These methods aim to provide clinical decision support at the cellular, tumor, and patient levels. Why it matters: AI-driven precision oncology can enable more personalized and effective cancer treatments, improving patient outcomes in the region and globally.
Shahar Harel, Head of AI at Quris, presented a BIO-AI approach to drug safety assessment using a 'patient-on-a-chip' platform. This platform simulates the human body and generates high-frequency microscopy and biochemical data on drug interactions, considering patient genomics and ethnicity. The data is used to train multimodal deep learning models to predict drug safety and provide patient-specific recommendations. Why it matters: This approach offers a potential alternative to animal models, promising faster and more personalized drug development while reducing safety concerns.
Tom M. Mitchell from Carnegie Mellon University discussed using machine learning to study how the brain processes natural language, using fMRI and MEG to record brain activity while reading text. The research explores neural encodings of word meaning, information flow during word comprehension, and how meanings of words combine in sentences and stories. He also touched on how understanding of the brain aligns with current AI approaches to NLP. Why it matters: This interdisciplinary research could bridge the gap between neuroscience and AI, potentially leading to more human-like NLP models.
Petar Stojanov from the Broad Institute of MIT and Harvard will give a talk on cancer data analysis, covering the fundamentals of cancer, the nature of large-scale data collected, and main analysis objectives. The talk will also address open questions in cancer data analysis and how machine learning and generative modeling can help. Stojanov's research focuses on applying machine learning to genomic analysis of cancer mutation and single-cell RNA sequencing data. Why it matters: Applying AI and machine learning to cancer research can lead to a better understanding of the disease and development of new therapies.
Ahmed Elhag, a PhD student at the University of Oxford, presented a new training procedure that approximates equivariance in unconstrained machine learning models via a multitask objective. The approach adds an equivariance loss to unconstrained models, allowing them to learn approximate symmetries without the computational cost of fully equivariant methods. Formulating equivariance as a flexible learning objective allows control over the extent of symmetry enforced, matching the performance of strictly equivariant baselines at a lower cost. Why it matters: This research from a speaker at MBZUAI balances rigorous theory and practical scalability in geometric deep learning, potentially accelerating drug discovery and design.
A report discusses using AI to optimize healthcare delivery across the entire medical process cycle, including pre-hospital screening, in-hospital treatment, and post-hospital rehabilitation. It considers optimal management of workflow, medical resources, and comprehensive healthcare coverage. Dr. Jingshan Li from Tsinghua University is the author, with extensive publications and experience in production and healthcare systems. Why it matters: AI-driven improvements to healthcare processes could lead to better resource allocation and enhanced patient outcomes across the GCC region.
A KAUST alumnus presented research on using large language models for complex disease modeling and drug discovery. LLMs were trained on insurance claims of 123 million US people to model diseases and predict genetic parameters. Protein language models were developed to discover remote homologs and functional biomolecules, while RNA language models were used for RNA structure prediction and reverse design. Why it matters: This work highlights the potential of LLMs to accelerate computational biology research and drug development, with a KAUST connection.
Researchers have developed robotic path-planning and control algorithms for minimally invasive surgery (MIS) that steer flexible needles, incorporating teleoperation and haptic feedback. An AI algorithm was designed to predict target motion due to respiratory movement, improving needle placement accuracy. GANs were used to generate synthetic images visualizing organ and tumor motion. Why it matters: This research demonstrates the potential of AI and robotics to enhance precision and adaptability in MIS, potentially reducing patient trauma and improving recovery times in the region and beyond.
Pascal Fua from EPFL gave a talk at MBZUAI on physics-based deep learning for medical imaging. The talk covered how self-supervision and knowledge of human anatomy and physics can improve deep learning algorithms when training data is limited. Applications discussed included endoscopic heart surgery, colonoscopy, and intubation. Why it matters: This highlights the growing importance of domain knowledge and self-supervision in overcoming data scarcity challenges for AI in healthcare applications within the region.
This research introduces a novel method using the Lateral Accretive Hybrid Network (LEARNet) to capture and analyze micro-expressions for mental health applications. The method refines both broad and subtle facial cues to detect mental health conditions like anxiety or depression. The authors also propose a neural architecture search (NAS) strategy to design a compact CNN for micro-expression recognition, improving performance and resource use. Why it matters: By integrating micro-emotion recognition with mental health estimation, the approach enables more accurate and early detection of emotional and mental health issues, potentially leading to improved well-being.
Video motion magnification amplifies subtle movements in video footage, making the imperceptible visible across various fields. In healthcare, it allows non-invasive monitoring of vital signs and micro-expressions. In engineering, it helps detect structural vibrations in infrastructure, while also being used in sports science, security, and robotics. Why it matters: The technology's ability to reveal hidden details has the potential to revolutionize diagnostics, monitoring, and decision-making in diverse sectors across the Middle East.