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Research project

COdesigning Trustworthy Autonomous Diabetes Systems (COTADS)

Project overview

COTADS explored how to increase trust of AI used for diabetes management inside and outside clinical settings during life transitions. AI is expected to provide a crucial role in the management of chronic conditions, yet technology-driven solutions are unlikely to be adopted. AI design must consider the complex medical, lifestyle and socio-technical needs at times of uncertainty and life transitions. COTADS brought together people with diabetes, clinicians, and data scientists in a novel co-design process for diabetes risk stratification. Using co-design, provenance, and explainable AI, we enhanced ways to ensure solutions are understandable, transparent, trustworthy, and beneficial.

Staff

Lead researcher

Professor Michael Boniface CEng, FIET

Professorial Fellow in Information Techn

Research interests

  • Artifical intelligence for health systems
  • Human centred interactive systems
  • Federated systems management 
Other researchers

Dr Chris Duckworth PhD, MSci

Senior Enterprise Fellow

Research interests

  • Artificial Intelligence for Health and Wellbeing
  • Explainable Machine Learning
  • Human Centred Interactive Systems

Mr Jakub Dylag

Research Engineer

Collaborating research institutes, centres and groups

IT Innovation

We develop and apply advances in IT technology, working with academic and industrial partners.

Digital Health and Biomedical Engineering

Our group brings together engineers and computer scientists to discover new approaches to diagnose and manage health and medical conditions.

Research outputs

Computational notebooks as co-design tools: engaging young adults living with diabetes, family carers, and clinicians with machine learning models
Amid Ayobi,
Jacob Hughes,
Christopher J Duckworth,
Jakub J. Dylag,
Sam James,
Paul Marshall,
Matthew Guy,
Anitha Kumaran,
Adriane Chapman,
Michael Boniface,
& Aisling Ann O'Kane
, 2023
DOI: 10.1145/3544548.3581424
Type: conference
Explainable machine learning for real-time Hypoglycemia and Hyperglycemia prediction and personalized control recommendations
Christopher Duckworth,
Matthew J. Guy,
Anitha Kumaran,
Aisling Ann O’Kane,
Amid Ayobi,
Adriane Chapman,
Paul Marshall,
& Michael Boniface
, 2022 , Journal of Diabetes Science and Technology
DOI: 10.1177/19322968221103561
Type: article
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