We envision a new research direction in AI for health to foster a transformative human centric paradigm, in which an emerging collaborative relationship between computers and humans will be created based on the ever-increasing power of AI. The computers will no longer just be used as tools, but instead act actively as a co-worker to offer sound, clear and evidence-based solutions to support clinicians and patients in clinical decision making and enhance clinical outcome.
The research will focus on three specific research themes:
- Trusted Data: Uncertainty, risk and security aware modelling of health data that will handle randomly varying nonlinearities, uncertain and missing measurements, where new approaches to estimate risks and predict outcomes of clinical decision support for informed choice in the face of risk and uncertainty will be targeted.
- Trusted AI: Explainable and secure AI that will contribute to the trust in the clinical decisions recommended by AI by investigating innovative AI-based health solutions that are explainable, secure, trustworthy and acceptable by humans.
- Trust in Human Factors: Human centred AI design that will investigate human factors and identify barriers in clinical acceptance to support a transformative human & AI collaboration experience based on trusted AI and data in the context of healthcare.
We have worked very closely with medical professionals and patients to use data driven technologies to empower the key stakeholders in healthcare, especially by allowing patients to have access to their own health information and improve their awareness of health risks by leveraging the latest AI technologies. This supports patients in a driving seat in health decision making.
Current research students are investigating topics including learning causality with data and enhancing the AI explainability through compositional solution finding. Future studies will assess the effectiveness and impact of the new AI techniques in terms of their usability and acceptability with humans in the context of healthcare.
We are working with a number of clinical partners including the NHS Glasgow, NHS Lanarkshire, NHS Lothian, Moorfields Eye Hospital, the InsightZen Group.
- K. Kondylakis, A Bucur, C. Crico, F. Dong, et al, Patient empowerment for cancer patients through a novel ICT infrastructure, Journal of Biomedical Infomatics, 101 (2020)
- P. Bailey, L. H. Mugridge , F. Dong, X. Zhang and A. M. Chater Randomised Controlled Feasibility Study of the MyHealthAvatar-Diabetes Smartphone App for Reducing Prolonged Sitting Time in Type 2 Diabetes Mellitus, International Journal of Environmental Research and Public Health 2020
- Wu, Y. Zhao, F. Parvinzamir, N. Ersotelos, W. Hui, F. Dong, Literature Explorer: effective retrieval of scientific documents through nonparametric thematic topic detection, The Visual Computer – August, 2019
- Kaba, N. McFarlane, F. Dong, N. Graf, X. Ye, Nephroblastoma Analysis in MRI Images, Image Analysis and Stereology, 38(2), 2019
- Yu, F. Dong, Semantic Lifting and Reasoning on the Personalised Activity Big Data Repository for Healthcare Research, International Journal of Web Engineering and Technology, 38(2), 2019
- Parvinzamir, Y. Zhao, Z. Deng, F. Dong, MyEvents: A Personal Visual Analytics Approach for Mining Key Events and Knowledge Discovery in Support of Personal Reminiscence, Computer Graphics Forum – 38(1), 2019.
- Ersotelos, A. Margioris, X. Zhang, F. Dong, Review of mobile applications for optimizing the follow-up care of patients with diabetes, Hormones, 2018
- Yang, F. Dong, et al, Improving Utility of GPU in Accelerating Industrial Applications with User-centred Automatic Code Translation, IEEE Transactions on Industrial Informatics, issue 99 2017
- Yang, D. Stankevicius, V. Marozas, Z.Deng, A. Lukosevicius, F. Dong, E. Liu, D. Xu: Lifelogging Data Validation Model for Internet of Things enabled Personalized Healthcare, IEEE Transactions on Systems, Man and Cybernetics: Systems, July 2016
- Yang, G. Clapworthy, F. Dong, V. Codreanu, D. Williams, B. Liu, J. Roerdink, Z. Deng, GSWO: A programming model for GPU-enabled parallelization of sliding window operations in image processing. Sig. Proc. Image Communication, 47: 332-345 (2016)