This project strengthens AI capability in healthcare by equipping healthcare professionals, researchers, and students with practical, hands-on skills in medical image analysis. To date, the focus has been on developing competency among researchers and technical users, delivering immediate impact in readiness for AI-enabled healthcare.
A flagship outcome was the 2D and 3D Medical Image Analysis Training using NVIDIA MONAI, conducted on 18–19 November 2025 at the Faculty of Computer Science and Information Technology, Universiti Malaya. The programme combined core theoretical foundations with hands-on sessions using real-world medical imaging datasets. Participants were trained across MONAI Core and MONAI Label workflows, including image classification, segmentation, annotation, and model deployment, providing end-to-end exposure to the AI imaging pipeline.
Building on strong participant response, the initiative is advancing towards a sustainable, industry-engaged training pathway for HPC service providers and advanced users. Future phases will involve industry practitioners as trainers, addressing demand for deeper learning in large-scale computation, optimisation, and AI deployment. Planned dual certification pathways, jointly endorsed by UM and industry partners, will be delivered through stackable microcredentials, supporting progressive skills upgrading and recognised accreditation.
Moving forward, this project serves as a seed initiative to consolidate UM’s leadership in AI-in-Medicine capacity building, extending beyond individual researchers to institutional capability. UM is increasingly recognised for its role in AI infrastructure and HPC management, which are critical enablers for leapfrogging AI-led research domains.
Importantly, this effort aligns closely with the Data Safe Haven initiative at PPUM, where secure clinical data repositories are complemented by robust computational infrastructure. Together, these form a foundational ecosystem supporting responsible data access, scalable analytics, and clinically relevant AI development—positioning UM as a national anchor for sustainable and scalable AI uptake in medicine.