This month we are very pleased to profile Associate Professor Jinman Kim. Jinman is from the School of Computer Science at the University of Sydney, with a strong involvement in the healthcare setting. The DHIN is pleased to have Jinman as a new member of the Leadership Group.
Please tell us a little bit about yourself. Hi DHIN members! My name is Jinman and I am an academic at the School of Computer Science, the University of Sydney. My research focuses on the use of machine learning (a subset of artificial intelligence – AI) in smart ways for health applications. I am very much involved in the healthcare setting, both as the Research Director, Telehealth and Technology Centre, Nepean Hospital and as the Academic Director, Healthcare Engineering (Westmead Academic Initiative).
How do you define digital health? A common definition of ‘digital health’ is the use of technology to enhance people’s access and control of their own, as well as to important health information. But from a computer scientist’s perspective, I think of ‘digital health’ as an opportunity to leverage the incredible advances in digital technologies to simplify and improve our health. These technologies are very exciting, including AI that are allowing automated decision support systems by learning from large amounts of data e.g., automated image interpretation, communication technology that are making remote consultations accessible e.g., video consultation with ‘face analysis’ to detect heart rates, to sensors that are capturing all sorts of digital data about our health and wellbeing seamlessly with smartphones and smartwatches.
What do you think will enable digital health projects and innovations to succeed? As with all great projects, it is the gathering of excellent people – from all relevant backgrounds – to flourish new ideas and challenge the old. My best experiences are when I collaborate with a multi-disciplinary team to tackle interesting and new problems that are enabled by new technology. Of course, this takes a lot of time and willingness to learn from each other, but it’s very rewarding.
I am excited to announce that starting next year, we are launching a new multi-disciplinary Master of Digital Health and Data Science degree that was developed together between the Faculty of Medicine and Health and the Faculty of Engineering. Students will learn to work together as data scientists and healthcare professionals to transform healthcare delivery with new data-driven health technologies.
What do you think are the biggest challenges facing digital health at the moment? A key challenge that I often see is with the adoption of the digital health technologies. I see many technologies that are very beneficial but are difficult to adopt due to the disruption to the current workflows. It requires learning to use new technology and also changing the way care are delivered. Technologies are difficult to understand too, with some AI algorithms such as ‘deep learning’ models that is considered to be a ‘black box’. What is encouraging is that there are now technologies and research happening to tackle these adoption challenges.
Do you have any interesting resources or helpful networks people should know about?
I like keeping up to date with startups in healthcare, especially AI startups. These startups are early indications of what digital health technologies are coming and for us and enable us to be ready! This is a good site – https://medicalstartups.org/top/ai/
I also follow the work from the focus group on “AI for Health”. The International Telecommunication Union (ITU) and World Health Organization (WHO) Focus Group on artificial intelligence for health (FG-AI4H) works to establish a standardized assessment framework for the evaluation of AI-based methods for health, diagnosis, triage or treatment decisions. https://www.itu.int/en/ITU-T/focusgroups/ai4h/Pages/default.aspx
A very big thank you to Jinman for taking the time to be our August 2021 feature!
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