New PhD Research to Focus on Equitable Healthcare Technologies

Hongyu Jin has joined the growing cohort of Social Equity in Engineering and Information Technology Scholars.

Since 2021, Melbourne Social Equity Institute has partnered with the Faculty of Engineering and Information Technology (FEIT) to offer annual PhD scholarships for new research projects that promote social equity through engineering and information technology.

The selected researchers undertake a PhD through FEIT while receiving additional research, mentoring and training support through Melbourne Social Equity Institute. To date, projects have focused on a range of challenges including misinformation, accessibility in digital technologies and housing solutions for displaced populations.

We recently welcomed Hongyu Jin to the Social Equity in Engineering and Information Technology program. Hongyu's research is focused on the role of Large Language Models (LLMs) in health equity.

LLMs are artificial intelligence systems trained on vast amounts of text data. They are increasingly being used in health care, with the potential to provide patients with continuous conversational support, personalised self-management advice, and supplementary clinical guidance. LLMs, however, risk inheriting or amplifying existing biases and inequities.

Hongyu's supervisors are Dr Ting Dang, an expert in human-centred machine learning and acoustic modelling for mobile health monitoring, and Associate Professor Mike Conway, a leading digital health researcher with a focus on the application of computational methods to public health research questions.

We asked Hongyu to tell us more about his research.

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Hongyu Jin

Thesis title: Trustworthy and Efficient Large Language Models for Stigma-Aware Healthcare

Prior to commencing your PhD, what were you doing?

Before commencing my PhD, I was studying the Master of Data Science at the University of Melbourne. During my master’s degree, I had the opportunity to work with Dr Ting Dang on research, which gave me valuable exposure to the research process and made me realise how much I enjoyed it. That experience strengthened my interest in pursuing a research career, especially in areas where machine learning can be applied to meaningful real-world problems.

What drew you to start a PhD on this topic?

I was drawn to this topic during the final year of my master’s degree. Because of a personal experience that led me to visit the emergency department, I had a much closer and more direct experience with the healthcare system than I had before. That experience made me think more seriously about the challenges faced in clinical settings, and about how technology could be used to make healthcare more accessible, fair and supportive. It also sparked my interest in the role of language in healthcare, especially how large language models might help while also needing to be designed carefully to avoid bias and stigma.

What outcomes are you hoping to achieve?

Through my research, I hope to contribute to the development of trustworthy and efficient large language models that can be applied more widely and at lower cost in healthcare settings. I would like my work to help make AI tools more practical, accessible, and affordable, so that they can support clinicians and patients in real-world environments. More broadly, I hope my research can contribute to safer and more equitable healthcare technologies.