AI applications in healthcare across the GCC — including medical imaging, clinical decision support, genomics, and health informatics from Gulf universities and hospitals.
KAUST researchers have discovered that a coral's resilience to rising temperatures is determined by the microorganisms living inside them. The study identifies specific combinations of microeukaryotes and bacteria that enhance heat resistance in corals. This finding provides valuable clues for developing coral probiotics to protect and restore coastal reefs. Why it matters: This breakthrough could lead to effective interventions to combat coral bleaching and preserve vital marine ecosystems in the Red Sea and beyond.
MBZUAI researchers introduce UniMed-CLIP, a unified Vision-Language Model (VLM) for diverse medical imaging modalities, trained on the new large-scale, open-source UniMed dataset. UniMed comprises over 5.3 million image-text pairs across six modalities: X-ray, CT, MRI, Ultrasound, Pathology, and Fundus, created using LLMs to transform classification datasets into image-text formats. UniMed-CLIP significantly outperforms existing generalist VLMs and matches modality-specific medical VLMs in zero-shot evaluations, improving over BiomedCLIP by +12.61 on average across 21 datasets while using 3x less training data.
MBZUAI releases BiMediX2, a bilingual (Arabic-English) Bio-Medical Large Multimodal Model, along with the BiMed-V dataset (1.6M samples) and BiMed-MBench evaluation benchmark. BiMediX2 supports multi-turn conversation in Arabic and English and handles diverse medical imaging modalities. The model achieves state-of-the-art results on medical LLM and LMM benchmarks, outperforming existing methods and GPT-4 in specific evaluations.
A new study introduces Sporo AraSum, a language model designed for Arabic clinical documentation, and compares it to JAIS using synthetic datasets and modified PDQI-9 metrics. Sporo AraSum significantly outperformed JAIS in quantitative AI metrics and qualitative attributes related to accuracy, utility, and cultural competence. The model addresses the nuances of Arabic while reducing AI hallucinations, making it suitable for Arabic-speaking healthcare. Why it matters: The model offers a more culturally and linguistically sensitive solution for Arabic clinical documentation, potentially improving healthcare workflows and patient outcomes in the region.
KAUST and MEWA's Aquaculture Development Program (ADP) showcased achievements at the 6th International Saudi Aquaculture Development Workshop. New fish nutrition formulations developed by KAUST Beacon Development (KBD) could save Saudi Arabia $417 million per year in aquaculture production costs by 2030 through improved feed conversion ratios. KBD has also established complete production cycles for Sobaity and Gilthead seabream under Red Sea conditions. Why it matters: These advancements boost Saudi Arabia's food security and promote sustainable aquaculture, reducing reliance on imports and diversifying the economy in line with Vision 2030.
KAUST researchers have discovered biological clues in the Wahbah Crater in Saudi Arabia that could provide insights into the possibility of life on Enceladus, one of Saturn's moons. The researchers isolated 48 bacterial strains from the crater, identifying two with an adaptability suitable for the extreme environment of Enceladus. These strains thrive in high temperatures, salinity, and alkaline pH levels, mimicking conditions on the Saturn moon. Why it matters: This study highlights the potential of Saudi Arabia's extreme environments as valuable models for detecting extraterrestrial life and strengthens the country's growing interest in space exploration.
KAUST and Oxford Nanopore Technologies have signed an MoU to collaborate on multi-omics research, building on previous work such as the NanoRanger technique developed by KAUST's Mo Li. KAUST will gain early access to Oxford Nanopore’s sequencing technology, while Oxford Nanopore will access KAUST's Core Labs. Why it matters: This partnership enhances KAUST's research capabilities in areas like rare diseases and desert agriculture, and provides Oxford Nanopore with a launchpad to engage with Saudi Arabia's research community.
KAUST has launched four Centers of Excellence (CoEs) focusing on Health, Sustainable Environment, Energy, and Economies of the Future, aligning with Saudi Arabia’s Vision 2030. One CoE, chaired by Professor Bernard Ghanem and co-chaired by Professor Juergen Schmidhuber, will focus on generative AI. These centers aim to deliver impactful solutions that directly contribute to national economic objectives. Why it matters: This initiative signifies a major push towards applied AI research and development within Saudi Arabia, particularly in generative AI, renewable energy, food security, and smart health.
KAUST has launched a Center of Excellence for Sustainable Food Security, led by Professor Mark Tester. The center aims to develop innovative solutions for food security challenges in arid regions, aligning with Saudi Vision 2030. It will focus on enhancing resource use efficiency, developing resilient crops, and promoting sustainable biosystems through interdisciplinary research and partnerships. Why it matters: This initiative will advance agricultural innovation in Saudi Arabia, supporting economic diversification and reducing reliance on food imports.
KAUST researchers have identified a gene, CLAMT1b, in pearl millet that affects its vulnerability to the parasitic weed Striga hermonthica. Pearl millet strains lacking CLAMT1b were found to be resistant to the weed, while those expressing the gene were susceptible. The gene's presence leads to the secretion of strigolactones, promoting interaction with Striga, but its absence does not harm symbiotic relationships with beneficial fungi. Why it matters: This discovery offers new breeding strategies to enhance pearl millet's resistance to parasitic weeds, bolstering food security in arid regions like Saudi Arabia and Africa where the crop is vital.
KAUST researchers have developed a genomic resource for Tausch’s goatgrass (Aegilops tauschii), a wild relative of wheat, by creating 46 high-quality genome assemblies. They compiled 493 genetically distinct accessions from an initial 900, collaborating with the Open Wild Wheat Consortium to select accessions with traits of interest, such as disease resistance and stress tolerance. Screening these assemblies helped identify rust resistance genes, including mapping a stem rust resistance gene to the Sr33 locus. Why it matters: This genomic resource will accelerate gene discovery in wheat, potentially improving modern wheat varieties and enhancing global food security.
KAUST researchers have been selected as finalists for two ACM Gordon Bell Prizes for high-performance computing. One project used NVIDIA GPUs to enhance genetic studies from the UK Biobank, achieving 133x speedup over existing software. The other developed an exascale climate emulator with higher spatial-temporal resolution than current models, demonstrated on supercomputers like Shaheen III. Why it matters: The recognition highlights KAUST's strength in high-performance computing research and its contributions to both genetic analysis and climate modeling.
KAUST and KFSHRC have developed NanoRanger, a new gene sequencing system for identifying mutations causing genetic diseases. NanoRanger offers a faster and simpler process to detect DNA abnormalities at base resolution, building on existing long-read sequencing technologies. The system is designed to be cheaper and faster, targeting diseases prevalent in Saudi Arabia due to consanguinity. Why it matters: The technology has the potential to improve diagnosis and treatment of Mendelian diseases, which are especially prevalent in the Arab world.
KAUST and the Saudi Ministry of Environment, Water and Agriculture (MEWA) are collaborating on the Aquaculture Development Program (ADP) to advance Saudi Arabia's food security goals under Vision 2030. The ADP aims to increase domestic seafood production to 530,000 tons annually by 2030 through sustainable aquaculture practices. KAUST is employing a multidisciplinary team and innovative approaches like Integrated Multitrophic Aquaculture (IMTA) to optimize resource use and minimize environmental impact. Why it matters: This partnership aims to transform Saudi Arabia's aquaculture sector, reducing reliance on imports and promoting economic diversification while preserving marine biodiversity.
KAUST researchers discovered a five-hectare bio-sedimentary formation of living stromatolites off Sheybarah Island in the Red Sea. These structures are microbial carbonates similar to fossils of early life and are only the second group found in normal marine settings. The stromatolites host a diverse microbial community, including reticulated filaments previously only found in caves. Why it matters: The discovery provides insights into early life on Earth and has implications for understanding potential life formation on Mars, while also creating a unique educational opportunity for tourism in Saudi Arabia.
This survey paper reviews recent literature on continual learning in medical imaging, addressing challenges like catastrophic forgetting and distribution shifts. It covers classification, segmentation, detection, and other tasks, while providing a taxonomy of studies and identifying challenges. The authors also maintain a GitHub repository to keep the survey up-to-date with the latest research.
KAUST and King Faisal Specialist Hospital and Research Centre (KFSHRC) are collaborating to develop an RNA sequencing tool to improve the diagnosis rate of genetic diseases. The tool analyzes RNA data to find aberrant transcripts and mutations, building on KFSHRC's clinical data and KAUST's computational expertise. The team has already solved cases that DNA sequencing alone could not, including a case of a young child with brain damage caused by a recessive gene mutation. Why it matters: This collaboration can improve disease management and preventative services in the region, directly contributing to Saudi Arabia’s national research priority of health and wellness.
Researchers at MBZUAI have developed DynaMMo, a dynamic model merging method for efficient class incremental learning using medical images. DynaMMo merges multiple networks at different training stages using lightweight learnable modules, reducing computational overhead. Evaluated on three datasets, DynaMMo achieved a 10-fold reduction in GFLOPS compared to existing dynamic methods with a 2.76 average accuracy drop.
The paper introduces MedPromptX, a clinical decision support system using multimodal large language models (MLLMs), few-shot prompting (FP), and visual grounding (VG) for chest X-ray diagnosis, integrating imagery with EHR data. MedPromptX refines few-shot data dynamically for real-time adjustment to new patient scenarios and narrows the search area in X-ray images. The study introduces MedPromptX-VQA, a new visual question answering dataset, and demonstrates state-of-the-art performance with an 11% improvement in F1-score compared to baselines.
Researchers at MBZUAI introduce FissionFusion, a hierarchical model merging approach to improve medical image analysis performance. The method uses local and global aggregation of models based on hyperparameter configurations, along with a cyclical learning rate scheduler for efficient model generation. Experiments show FissionFusion outperforms standard model souping by approximately 6% on HAM10000 and CheXpert datasets and improves OOD performance.
Researchers at MBZUAI have introduced TiBiX, a novel approach leveraging temporal information from previous chest X-rays (CXRs) and reports for bidirectional generation of current CXRs and reports. TiBiX addresses two key challenges: generating current images from previous images and reports, and generating current reports from both previous and current images. The study also introduces a curated temporal benchmark dataset derived from the MIMIC-CXR dataset and achieves state-of-the-art results in report generation.
Researchers at MBZUAI have introduced MedMerge, a transfer learning technique that merges weights from independently initialized models to improve performance on medical imaging tasks. MedMerge learns kernel-level weights to combine features from different models into a single model. Experiments across various medical imaging tasks demonstrated performance gains of up to 7% in F1 score.
This paper introduces a new Single Domain Generalization (SDG) method called ConDiSR for medical image classification, using channel-wise contrastive disentanglement and reconstruction-based style regularization. The method is evaluated on multicenter histopathology image classification, achieving a 1% improvement in average accuracy compared to state-of-the-art SDG baselines. Code is available at https://github.com/BioMedIA-MBZUAI/ConDiSR.
Researchers from MBZUAI have developed XReal, a diffusion model for generating realistic chest X-ray images with precise control over anatomy and pathology location. The model utilizes an Anatomy Controller and a Pathology Controller to introduce spatial control in a pre-trained Text-to-Image Diffusion Model without fine-tuning. XReal outperforms existing X-ray diffusion models in realism, as evaluated by quantitative metrics and radiologists' ratings, and the code/weights are available.
KAUST researchers have developed a new synthetic biology process using metabolically engineered algae to produce fragrant sesquiterpenoids, the core compounds in agarwood and other perfumes. The process, developed by the Lauersen and Szekely groups, achieved yields 25 times higher than previous methods and allows for the synthesis of 103 types of fragrant sesquiterpenoids. It also incorporates an energy-efficient nanofiltration step and operates at room temperature with minimal waste. Why it matters: This sustainable bioprocess offers a green alternative to environmentally damaging harvesting of natural resources for the $44 billion fragrance industry, with potential applications in drug development.
KAUST researchers have developed new techniques to produce nutritious microalgae in industrial volumes using seawater-adapted Spirulina and Chlorella strains. This innovation eliminates the need for freshwater, making algae-based livestock feed production sustainable and economically viable. The new Saudi Center for Algal Biotechnology Development and Aquaculture will scale up operations from a 1,000 square meter pilot plant to 42,000 square meters. Why it matters: This could help Saudi Arabia decrease its dependency on imported feed and raw food materials, aligning with its Vision 2030 goals for increased domestic food security.
MBZUAI researchers introduce BiMediX, a bilingual (English and Arabic) mixture of experts LLM for medical applications. The model is trained on BiMed1.3M, a new 1.3 million bilingual instruction dataset and outperforms existing models like Med42 and Jais-30B on medical benchmarks. Code and models are available on Github.
KAUST researchers, in collaboration with Spanish scientists, have released the Global Ocean Gene Catalog 1.0, the world's largest open-source catalog of marine microbes. The catalog, created using the KAUST Metagenomic Analysis Platform (KMAP), matches microbial class with gene function, geographic location, and habitat type, including 317 million unique gene clusters. The catalog analyzes 2102 ocean samples taken from different depths and locations around the world. Why it matters: This resource will enable researchers to investigate ocean ecosystems, track pollution impact, and explore biotechnology applications, potentially driving significant advances in fields like antibiotic discovery and plastic degradation.
Researchers introduce TomFormer, a transformer-based model for accurate and early detection of tomato leaf diseases, with the goal of deployment on the Hello Stretch robot for real-time diagnosis. TomFormer combines a visual transformer and CNN, achieving state-of-the-art results on KUTomaDATA, PlantDoc, and PlantVillage datasets. KUTomaDATA was collected from a greenhouse in Abu Dhabi, UAE.
KAUST researchers found Y-series nonfullerene acceptors enhance the outdoor stability of organic solar cells, enabling energy-efficient windows. They also used satellite data to show managed vegetation can mitigate rising temperatures across Saudi Arabia's agricultural regions. Additionally, they developed DeepKriging, a deep neural network, to solve complex spatiotemporal datasets and tested it on air pollution. Why it matters: This research addresses critical challenges in renewable energy, climate change, and AI data privacy relevant to Saudi Arabia and the broader region.
Researchers propose a universal anatomical embedding (UAE) framework for medical image analysis to learn appearance, semantic, and cross-modality anatomical embeddings. UAE incorporates semantic embedding learning with prototypical contrastive loss, a fixed-point-based matching strategy, and an iterative approach for cross-modality embedding learning. The framework was evaluated on landmark detection, lesion tracking and CT-MRI registration tasks, outperforming existing state-of-the-art methods.
A KAUST study explains how corals control their symbiotic algae using nutrient competition, tracking carbon and nitrogen isotopes. The research shows that cnidarians limit nitrogen available to the algae, preventing overgrowth and maintaining a balanced symbiosis. This mechanism evolved independently in corals, sea anemones, and jellyfish. Why it matters: The findings have implications for coral reef restoration efforts like the KAUST Reefscape Restoration Initiative by disrupting traditional views of symbiosis.
Researchers from KAUST, King Faisal Specialist Hospital, and collaborators have developed a new method to predict cardiometabolic disease risk in underrepresented ethnic populations using genetic information and public databases. The study focused on Arab communities and created a framework to determine polygenic scores for more accurate heart disease prediction. The framework was validated using records of over 5,000 Arab patients, demonstrating that genetic risk complements conventional risk factors. Why it matters: This research addresses a critical gap in genomic data for non-European populations, potentially leading to more effective and personalized healthcare strategies in the Arab world and beyond.
KAUST, AEON Collective, and KAPSARC released a report during MENA Climate Week assessing climate change impacts in Saudi Arabia under a 3°C warming scenario. The report finds that Saudi Arabia is experiencing climate change at an accelerated pace, with potential temperature increases of up to 5.6°C by the end of the century. It highlights the need for policy choices and adaptation strategies to mitigate impacts on ecosystems, urban environments, and human health. Why it matters: This comprehensive analysis provides critical insights for Saudi Arabia to inform its climate policies and adaptation efforts, addressing challenges like coral bleaching, desert expansion, and increased health risks.
This paper introduces a domain generalization (DG) method for Diabetic Retinopathy (DR) classification that maximizes mutual information using a large pretrained model. The method aims to address the challenge of domain shift in medical imaging caused by variations in data acquisition. Experiments on public datasets demonstrate that the proposed method outperforms state-of-the-art techniques, achieving a 5.25% improvement in average accuracy.
This paper introduces Pulmonary Embolism Detection using Contrastive Learning (PECon), a supervised contrastive pretraining strategy using both CT scans and EHR data to improve feature alignment between modalities for better PE diagnosis. PECon pulls sample features of the same class together while pushing away features of other classes. The approach achieves state-of-the-art results on the RadFusion dataset, with an F1-score of 0.913 and AUROC of 0.943.
MBZUAI researchers introduce XrayGPT, a conversational medical vision-language model for analyzing chest radiographs and answering open-ended questions. The model aligns a medical visual encoder (MedClip) with a fine-tuned large language model (Vicuna) using a linear transformation. To enhance performance, the LLM was fine-tuned using 217k interactive summaries generated from radiology reports.
KAUST and King Faisal Specialist Hospital and Research Centre (KFSH&RC) are collaborating to develop bioelectronic sensors for rapid pathogen detection. These sensors aim to provide cheap and accurate results, potentially replacing conventional lab tests. A COVID-19 saliva test developed by KAUST researchers showed comparable sensitivity to PCR tests with a 15-minute turnaround. Why it matters: This partnership accelerates the development of novel diagnostic tools, which could improve healthcare accessibility in remote areas and low-income countries within the region.
Researchers from MBZUAI have developed EchoCoTr, a novel spatiotemporal deep learning method for estimating left ventricular ejection fraction (LVEF) from echocardiograms. EchoCoTr combines CNNs and vision transformers to overcome the limitations of each when applied to medical video data. The method achieves state-of-the-art results on the EchoNet-Dynamic dataset, demonstrating improved accuracy compared to existing approaches, with code available on GitHub.
KAUST Associate Professor Peiying Hong has developed a wastewater treatment method using anaerobic membrane bioreactor (AnMBR) technology, which converts organic carbon into methane. In partnership with MODON, a pilot program is operational in Jeddah, treating 23,000 liters of wastewater daily using UV light and hydrogen peroxide for disinfection. This system produces clean water suitable for agriculture and biomass for fertilizer, with a smaller footprint and lower energy consumption than traditional aerobic methods. Why it matters: The AnMBR technology aligns with Saudi Vision 2030's water reuse objectives, reducing reliance on energy-intensive desalination and offering a sustainable solution for water-stressed regions.
KAUST researchers have developed a CRISPR-Cas system using a heat-stable Cas13 protein (TccCas13a) from Thermoclostridium caenicola, compatible with RT-LAMP for rapid viral detection. The new assay, named OPTIMA-dx, enhances the specificity of RT-LAMP tests by reducing false positives in SARS-CoV-2 detection. The team, led by Dr. Magdy Mahfouz and doctoral student Ahmed Mahas, is transitioning the product to a startup phase for commercialization. Why it matters: This innovation could significantly improve point-of-care diagnostics for COVID-19 and other infections by providing a more accurate and easier-to-use testing method.
KAUST researchers, in collaboration with the Salk Institute and Altos Labs, have identified a class of RNA (LINE-1) that, when compromised, leads to accelerated aging, as seen in progeria. They devised an antisense RNA strategy to block the aberrant function of L1 RNA, reversing the disease in mice and patient-derived cells. Published in Science Translational Medicine, the research suggests that targeting LINE-1 RNA could treat progeroid syndromes and other age-related diseases. Why it matters: This RNA-based approach provides a potential therapeutic avenue for treating premature aging diseases and extending human health span in the region and globally.
Researchers at KAUST and international collaborators have published a framework in Nature Microbiology for using microbiomes to protect wildlife. The framework outlines a path from laboratory research to large-scale applications of microbiome solutions for threatened ecosystems. It addresses ethical considerations and risk assessment for applying environmental probiotics. Why it matters: This framework provides a science-based guide for responsible research and development of microbiome solutions to combat global biodiversity loss, particularly in sensitive marine ecosystems like coral reefs.
KAUST's Destination Deep Tech program has concluded with five international startups—CeEntek, Hopu, Insignes-Labs, Pasqal, and Proteinea—expanding into the MENA region. These startups, chosen for their advanced technologies, address societal challenges like food security, climate change, and health. Pasqal signed an MoU with Aramco and is collaborating with KAUST's Extreme Computing Research Center, while Insignes-Labs is piloting with Red Sea Farms. Why it matters: This program exemplifies KAUST's commitment to fostering a deep tech ecosystem in Saudi Arabia, attracting global talent to align with Vision 2030 and tackle critical regional and global challenges.
KAUST researchers have developed an artificial electronic retina mimicking the behavior of rod retina cells, utilizing a hybrid perovskite material (MAPbBr3) embedded in PVDF-TrFE-CEF. The photoreceptor array, made of metal-insulator-metal capacitors, detects light intensity through changes in electrical capacitance. Connected to a CMOS-sensing circuit and a spiking neural network, the 4x4 array achieved around 70 percent accuracy in recognizing handwritten numbers. Why it matters: This research paves the way for energy-efficient neuromorphic vision sensors and advanced computer vision applications, potentially revolutionizing camera technology.
This paper introduces a self-supervised contrastive learning method for segmenting the left ventricle in echocardiography images when limited labeled data is available. The approach uses contrastive pretraining to improve the performance of UNet and DeepLabV3 segmentation networks. Experiments on the EchoNet-Dynamic dataset show the method achieves a Dice score of 0.9252, outperforming existing approaches, with code available on Github.
Researchers at KAUST have developed a nanocomposite material that converts X-rays into light with nearly 100% efficiency. The material combines a metal-organic framework (MOF) containing zirconium with an organic TADF chromophore. This design achieves high resolution and sensitivity in X-ray imaging, potentially reducing medical imaging doses by a factor of 22. Why it matters: This innovation could lead to more efficient and safer medical imaging and security screening technologies in the region and beyond.
KAUST has inaugurated the world's first Coral Probiotics Village (CPV) in the Red Sea, a natural laboratory for coral reef research. The CPV is located 20 km off the KAUST campus and will focus on developing solutions for coral preservation and restoration using Beneficial Microorganisms for Corals (BMCs). Researchers aim to mitigate the impacts of thermal stress and pathogens on corals, potentially preventing coral mortality. Why it matters: This initiative positions KAUST as a leader in marine biotechnology and offers innovative approaches to protect coral reefs in the Red Sea and worldwide.
KAUST researchers have discovered that the bacterium Enterobacter sp. SA187, found in desert plants, enhances plant salt tolerance by triggering sulfur metabolism. Salt stress prompts the bacteria to release sulfur metabolites, which then generate the antioxidant glutathione in the plant, protecting it from salt-induced damage. A KAUST startup aims to commercialize SA187 as a probiotic treatment for seeds and crops. Why it matters: This research offers a biotechnological approach to enable saline agriculture, which is crucial for water-scarce regions like Saudi Arabia that rely on energy-intensive desalination.
KAUST researchers are using 3D printing with a novel calcium carbonate ink to create coral support structures that accelerate coral restoration. Their approach, named 3D CoraPrint, involves printing coral microfragments onto the structure, offering a head start for reef recovery. Two methods were developed: printing a mold for reproduction and direct printing for customization. Why it matters: This eco-friendly technique provides a potentially scalable solution to combat coral reef degradation, leveraging advanced materials and fabrication for ecological conservation in the region and beyond.