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As Hong Kong sets its sights on becoming a hub for artificial intelligence (AI), the city’s higher education institutions are already making strides ahead in this field. Since 2021, HKBU has leveraged NVIDIA’s groundbreaking Graphics Processing Unit (GPU) expertise to propel five research projects. Their joint symposium, launched in 2022, has become a premier platform for academia and industry, fostering transdisciplinary collaboration to drive AI applications across diverse sectors.

During the most recent symposium, held in February and March 2025, Professor Simon See, Global Head and Chief Solution Architect, NVIDIA AI Technology Center, delivered a keynote presentation on the transformative potential of health-tech, titled “The New Work Horse: Agentic AI”. We had the privilege of sitting down with him to gain insights into the rapidly evolving world of AI in healthcare.

When expertise meets innovation
In 2011, Professor See joined NVIDIA, marking the beginning of a powerful union that continues to push the boundaries of innovation. The combination of Professor See’s expertise in architectural engineering and NVIDIA’s pioneering spirit in AI and GPU markets has sparked a synergy that unlocks unique perspectives and new opportunities.

Professor See brings to the table a wealth of knowledge and experience, gained through his impressive career with leading organisations such as SGI, DSO National Lab. of Singapore, IBM, International Simulation Ltd. (UK), Sun Microsystems and Oracle. With a Ph.D. in Electrical Engineering and Numerical Analysis from the University of Salford, United Kingdom, he has established himself as a respected expert in high-performance computing, big data, AI, machine learning, computational science, applied mathematics and simulation methodology. His prolific research output includes more than 200 published papers in these areas, earning him a variety of accolades. 

NVIDIA, with its rich history of innovation, has evolved from a company known for graphics cards and video game software into a giant in the AI revolution. The company’s foray into healthcare is particularly noteworthy, as it leverages accelerated computing to build solutions that address the industry’s pressing needs for personalised medicine, next-generation clinics, enhanced quality of care, and biomedical breakthroughs. With exceptional talent like Professor See on board, NVIDIA is poised to transform the healthcare and life sciences sectors with novel AI-powered solutions in digital health, biopharma, genomics, medical devices, and medical imaging.

Addressing healthcare challenges with AI
The world is facing a multitude of healthcare-related challenges, including a surge in healthcare spending, widening global health disparities, a shortage of healthcare manpower, and deteriorating mental health and well-being worldwide. However, Professor See believes that the use of AI may hold the key to solving these problems.

Professor See noted that AI has made tremendous progress over the past few years, with its performance in certain areas surpassing human capabilities. “The success of these technologies is not solely due to scientific improvement and breakthroughs,” he remarked. “It is also because they have been widely adopted by companies and enterprises.” Today, AI applications have permeated a variety of healthcare segments, including disease treatment, drug development, gene editing, robot-assisted surgery, among many others.

Enhancing diagnostic efficiency
For example, the successful application of AI in medical imaging offers multifaceted benefits. AI algorithms can rapidly analyse large amounts of imaging data, identifying patterns and abnormalities that may be overlooked by human eyes, thereby significantly enhancing diagnostic accuracy and efficiency. This greatly aids in the detection and treatment of diseases, such as cancer and cardiovascular conditions, by providing consistent and precise image analysis. Furthermore, the integration of AI enables the combination of patients’ imaging data with their medical history and genetic information, allowing medical professionals to create personalised treatment plans that improve patient outcomes. “Many medical devices, modalities and applications can become AI-enabled. In other words, every medical device will become robotic and perform AI actions or skills in real time,” Professor See added.

Speeding up drug discovery
AI is also revolutionising the field of drug discovery. It can be used for target identification, de novo drug design, molecular simulations and predictions, streamlining development, and so forth. Traditionally, drug development has been a complex and time-consuming endeavour that relies on the experience of drug developers and trial-and-error experimentation. However, with the advent of accelerated computing, researchers can now virtually model millions of molecules and screen hundreds of potential drugs simultaneously, reducing costs and speeding time to solutions.

In 2024, NVIDIA launched the open-source NVIDIA® BioNeMo™ Framework, a next-generation platform designed for pharmaceutical industry leaders, academic pioneers, and AI researchers to advance drug discovery and accelerate molecule design. According to Professor See, generative AI dry labs are accelerating drug discovery by three years.

Decoding human brains with AI
Professor See also discussed the cutting-edge generative AI, a subset of AI that utilises generative models to produce text, images, videos, or other forms of data by learning the underlying patterns and structures of their training database. “The advancement of generative AI is one of the most fascinating things in the past 18 months,” he commended.

Researchers have applied this exciting technology to reconstruct images and even videos from human brains using fMRI data. Such pioneering work, first initiated by a group of scientists from the National University of Singapore, harnesses the power of recent advances in large models and artificial general intelligence to advance the field of brain decoding. This breakthrough has the potential to enable doctors to understand the thoughts of patients who are unable to express themselves. Professor See cited a remarkable example in which AI helped a paralysed woman to speak for the first time in 18 years by detecting her unique brain signals and translating them into intelligible sentences. 

Collaborating with universities to advance research
In closing, Professor See shared his vision for collaboration with higher education institutions. “First of all, as a mathematician, I wish to understand the fundamentals of AI and use mathematics to explain its principles,” he stated. Professor See’s academic journey began with mathematics and physics, gradually expanding into engineering, computational science and ultimately AI, as he became acquainted with high-performance computing during his career. “The second thing is to apply AI in diverse domains, such as climate science and biological science. As universities possess a rich pool of talents, we would like to leverage on this to further our research in these areas.” While acknowledging the interesting research ideas on AI proposed by scholars in Hong Kong, Professor See noted that the territory’s infrastructure limitations may pose a challenge to developing large-scale models and projects.

Throughout his presentation and our conversation, Professor See highlighted the rapid pace of AI evolution. “The technology is changing extremely fast,” he reiterated. “AI is going to touch on everything, influencing domains such as healthcare, the legal profession and finance.” To thrive in this new landscape, Professor See encouraged students, irrespective of their academic focus, to remain adaptable, acquire new skills quickly, and learn to integrate AI into their respective fields. “Multidisciplinary learning at university allows students to look at things from different perspectives. You can borrow ideas from one field and apply them to others. This is extremely useful.” Well, this is good news for our students, as transdisciplinary education has been emphasised here at HKBU.