Professor Dr Nazia Abdul Majid

Institute of Biological Sciences

Faculty of Science

This project conducted a systematic benchmarking of global and national AI-in-Medicine roadmaps to inform Malaysia’s strategic direction for healthcare AI adoption. By examining leading jurisdictions across policy, regulation, data governance, implementation, and innovation capacity, the study identifies practical lessons for moving from isolated pilots to trusted, system-wide deployment.

Three key insights emerged from the benchmarking exercise. First, successful AI healthcare ecosystems invest heavily in building a transdisciplinary talent pipeline, particularly young technopreneurs who combine technical expertise with clinical understanding, regulatory awareness, and innovation capability. Such talent is essential for sustaining AI solutions beyond proof-of-concept and translating them into deployable healthcare products.

Second, the benchmarking reinforces that co-creation with clinicians from the outset is critical to earning trust within the medical fraternity. Jurisdictions with mature AI adoption embed clinicians early in problem definition, validation, and workflow design, ensuring that AI tools address real clinical needs rather than being perceived as externally imposed technologies.

Third, the study highlights the growing importance of AI literacy as a foundation for the innovation pipeline. Industry increasingly seeks professionals who can understand, evaluate, and responsibly deploy AI, even if they are not developers themselves. In this context, Universiti Malaya is well positioned to lead through structured AI literacy and training offerings that bridge clinicians, engineers, and industry, strengthening the end-to-end innovation ecosystem.

The project also underscores the value of science communication and advocacy, with dedicated talent synthesising complex global AI trends into accessible insights for medical practitioners, supporting informed engagement and expectation management.

From a technology standpoint, the benchmarking identifies medical imaging, particularly X-rays and pathological slides, as high-impact entry points for Malaysia. These modalities support both diagnostic workflows and upskilling of early-career clinicians, enabling AI-assisted learning alongside clinical practice.

Overall, the benchmarking positions Malaysia at a mid-level readiness stage, with strong policy intent but gaps in workforce readiness and operational governance. The project provides clear direction, emphasising AI literacy, talent development, clinician co-creation, and targeted adoption pathways as the levers for ethical, trusted, and scalable AI in healthcare.