Assistant Professor Ngai-Man (Man) Cheung is a researcher with the Information Systems Technology and Design Pillar at the Singapore University of Technology and Design. His research interests are image / signal analysis, computer vision and machine learning with applications to health care. His team has invented an early skin cancer detection technology using computer vision and AI. The technology has been transferred to industry. His team has also invented a novel chronic wound monitoring technology. The work has led to a start-up company and several awards, such as the Best Design Award – MIT Hacking Medicine@SG and the first prize at Accenture HealthTech Challenge (Singapore) recently.

What is the biggest question facing your field?

In Healthcare Engineering, an important question has been how to develop accessible early disease diagnosis and monitoring technology that is usable by the general public. The elder population and the number of people with chronic health issues are increasing rapidly. On the other hand, supporting the growing number of patients with chronic health condition is becoming increasingly harder, as healthcare systems in many developed countries are already under manpower shortage. This causes increasing numbers of delayed diagnosis, additional costs, and loss of lives and livelihoods. It is important to understand how to use technology to assist healthcare workers and clinicians in some routine and time consuming labour. In addition, it is imperative to understand how to use technology to empower lay caregivers / family members to play more active roles in monitoring the conditions of elderly and chronic disease patients.

Why is it significant?

Accessible health monitoring and surveillance systems lead to early diagnose of diseases, significantly increasing the survival and recovery rate. If detected in the early stage, many health issues (e.g., pre-diabetes) can revert to normal. For chronic disease patients, improved assessment and monitoring can prevent the development of complications. All this can lead to substantial reduction in healthcare cost and enormous social impact.

Where is the answer likely to come from?

There is huge potential for machine learning, artificial intelligence (AI) and signal analysis to help improve diagnosis of disease and monitoring of chronic health conditions. Recently, state-of-the-art AI has matched the accuracy of clinicians for diagnosis of a few diseases. In addition, smartphones are a remarkably accessible platform that can be leveraged to provide health monitoring and early diagnosis solutions to healthcare workers and family members of chronic disease patients.

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