Staff Profile: Up close with Assistant Professor Yeo Si Yong

By Sanjay Devaraja, Editor, The LKCMedicine

Dr Yeo Si Yong joined LKCMedicine on 12 September 2023 as an Assistant Professor of Digital Health. With a PhD in Computational Engineering, Asst Prof Yeo specialises in artificial intelligence (AI)-based biomedical data analysis. His expertise includes computer vision and the design of AI systems for medical imaging, video analysis and medical diagnostics. Asst Prof Yeo's expertise, teaching experience, and strong research track record position him to contribute significantly and enhance LKCMedicine's prominence in the field of digital health.

Asst Prof Yeo elaborates on his research focus and the importance of AI in the field of medicine.

As Assistant Professor of Digital Health at LKCMedicine, please share with us on your role.

I work at the Medical Vision and Artificial Intelligence Technologies or MVAIT, a research group working on research projects in medical data interpretation, and AI in medicine. My current research focuses on the design and use of AI systems for medicine, which includes AI for medical imaging data, computer vision, health informatics and medical data interpretation.

What is the mission of MVAIT?

The mission of MVAIT is to develop AI models that can transform the healthcare industry, complement the expertise of medical doctors and introduce efficient and affordable medical technologies to the society. The lab where I work at endeavours to advance medical AI with a specific focus on clinical translation and medical data interpretation.

What sparked your interest in AI?

There are different methods to model medical data and AI is just one of those. However, the constant effort from the community to enhance AI technology has made AI a very versatile and accurate modelling tool. The flexibility of AI tools to transfer knowledge learned from a specific domain to a different type of dataset is interesting and important in the medical industry. For example, an AI tool trained to classify computed tomography (CT) imaging data can be easily adapted to classify X-ray dataset.

Why is it essential for medical students today to be knowledgeable on digital health and AI?

As digital health and AI have transformed the nature of medicine, I believe it is important for medical students to be familiar with medical AI and digital clinical tools to be ready for clinical practice and to serve the community better. Digital health and AI may increase patient autonomy by improving the accuracy of diagnosis and allowing patients to follow preferred treatment or medications. However, to enable clinicians to use AI tools effectively in clinical practice, explainability and interpretability should be incorporated to the tools.

Despite the challenges you face in your research, how do you wish to contribute to the field of digital health?

Some of the challenges include the availability of medical data, and the readiness of health industry to use AI in clinical practice. Nevertheless, my wish is to enhance AI technology for medicine. In particular, I would like to help translate such technology to clinical practice.

What or who motivates you to achieve or aspire for the greater good through research?

Knowing that there are different communities in the world which do not have access to good medical systems, it is important that medical professionals and researchers understand that they can do a part to help change the situation. I believe that medical research when conducted with such an intention can be more effective and introduce good and affordable medical systems to society.

Do share us with us how you unwind after a packed day at work/in the labs?

I listen to music and do sports for leisure. I enjoy different types of music and team sports. Team sports allow me to gain insights to team dynamics which are important for a good research lab. I also read research articles from different fields to be acquainted with the different tools that may be adaptable for medicine.