AI for Social Good

When Professor Victor Li On-kwok and Dr Jacqueline Lam Chi-kei of the Faculty of Engineering started working together more than a decade ago, their aim was simple: how can big data be harnessed for positive change in our societies? Professor Li brought information engineering expertise, Dr Lam urban planning. Their initial focus was on air pollution given its detrimental impacts on health and the environment and they began to work with various departments in the University of Cambridge.

Emerging from that work have been ongoing collaborations that now also cover AI in Medicine, as well as the HKU-Cambridge AI to Advance Well-being and Society and HKU-Cambridge AI for Neuro-disease research platforms.

Over the past three consecutive years, for instance, they have been awarded an exceptional three Healthy Longevity Catalyst Awards by the US National Academy of Medicine (NAM) for applying AI and big data to the challenge of early detection and treatment of patients with Alzheimer’s Disease (AD).

“Our aim has been to use AI- and data-driven research models and interdisciplinary approaches to improve people’s health and quality of life,” Professor Li said. “These models have become more powerful in recent years thanks to advancements in computational capacity and the availability of more big data.”

Combing for biomarkers

Their focus on AD has been inspired by the fact that around 50 million people worldwide currently suffer from AD and related forms of dementia, including 10 million people in China, resulting in irreversible brain damage. Despite the urgent need for treatment solutions, there is a lack of effective restorative treatments or preventative therapeutics for AD. One treatment, Aduhelm, was approved by the US Food and Drug Administration in 2021, but it has side effects and its effectiveness remains inconclusive.

The research led by Professor Li and Dr Lam, with collaborators Professor Illana Gozes from Tel Aviv University, Dr Yang Han, and Dr Jocelyn Downey from HKU Engineering, aims to accelerate the search for more effective AD drugs by applying new causal AI techniques and domain-specific pathological knowledge. Their approach combines AI with neuroscience and immunology expertise and has led to a breakthrough in methodology by creating a biomedical graph that incorporates genetic mutations and pathological knowledge. This is expected to improve the speed and accuracy of drug discovery for AD.

They are also preparing to develop a model to comb huge health datasets from the US and the UK for biomarkers, such as genetic, protein biomarkers, and linguistic markers, that could determine if the disease can be detected before the onset of symptoms. Linguistic markers, in particular, could detect if a person’s language is becoming impaired, which might be a very early signal of Alzheimer’s. The team hope to identify such new linguistic biomarkers for different languages.

“Our linguistic team, including Dr Lawrence Cheung from the Chinese University of Hong Kong and Professor James Rowe from Cambridge, shall work with us to develop a standardised set of assessment tools so we can collect natural language data from patients while also making good use of the available linguistic data,” Dr Lam said. This work will be supported by their 2023 NAM award, with additional funding expected from other sources.

Recently, the HKU team also established the HKU-Cambridge AI for Neuro-disease research platform with Professor David Rubinstein and Professor James Rowe of Cambridge, to apply their AI model to predict AD and possibly other neural diseases such as Parkinson’s and Huntington’s Disease.

Air pollution alerts

Other major achievements include a HK$50 million Theme-based Research project awarded in 2017 by the Research Grants Council that enabled them to estimate air pollution at the individual street level, thus allowing a fine-grained assessment of health impacts.

This work was inspired by the limitations of the current air quality alert system. Hong Kong has only 18 air quality monitoring stations, but the air quality can vary a lot between them. The team sought to fill in the blanks by inputting other information related to air quality – such as traffic congestion, wind direction and speed, the presence of tall buildings, and temperature and humidity – to create 110,000 virtual stations that can estimate the air pollution on any given street.

“Increasingly, our mission has been reinforced in different parts of the world where there have been increasing alerts on the effects of air pollution on health and mortality. For a while, attention was mostly on greenhouse gases, but now people realise the health impacts of air pollution have not gone away,” Professor Li said.

“AI is just a tool and it can be very meaningful and powerful if we aim to make good use of it to improve the situation for weak and vulnerable people in society, while minimising its undesirable effects,” Dr Lam said.