AI for diabetes - enhancing diagnosis and personalised care

Summary

Diabetes is a growing global epidemic, with cases rising from 200 million in 1990 to 830 million in 2022, according to the WHO. Type 2 diabetes (T2D), which is predominantly lifestyle related, now affects almost 10% of the global population and is expected to rise exponentially over the next 20 years. Mortality rates from diabetes have been increasing since 2000, and gestational diabetes is also a growing concern, currently affecting around 1 in 20 pregnancies in the UK.

Preventing and detecting T2D early is essential to avoid additional complications, reduce long-term damage, and ultimately save lives.

Clincians are moving beyond a ‘one-size-fits-all’ approach, seeking to better classify subtypes of T2D using readily available clinical parameters to deliver personalised care.

This project seeks to leverage AI to transform T2D care by combining heterogeneous datasets to deliver:

-  improved diagnosis and prediction of disease progression,

-  improved, personalised treatment options and

-  better patient outcomes.

Data from sources such as NITRE: Northern Ireland Trusted Research Environment, GPIP: General Practitioner Intelligence Platform, the Honest Broker Service NI and deprivation data will form the foundation of this research. The project will apply advanced AI methods to explore complex interrelationships, and identifiy patterns and markers that underpin effective diagnosis, intervention and treatment strategies.

By combining AI innovation with healthcare needs, this research aims to improve patient outcomes, reduce NHS costs, and address the growing challenge of diabetes. If offers an interdisciplinary opportunity to integrate AI with biological science, driving impactful healthcare solutions.

This project is a 4-year PhD project with enhanced training and 3+ month placement, which is fully funded by UKRI BBSRC through the NI Landscape Partnership in AI for Bioscience (NILAB) Programme, delivered by Queen’s University Belfast and Ulster University. Details of the enhanced training will be available later at qub.ac.uk/nilab/.  NILAB aims to bridge the gap between biology and artificial intelligence to accelerate bioscience discovery and foster effective collaboration between academia, industrial partners, and government bodies. NILAB’s mission is to train the next generation of researchers to develop and use AI to uncover the rules of life, addressing challenges in human health, animal welfare, and sustainable food systems.

This project is open to both home and international applicants on a competitive basis

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Clearly defined research proposal detailing background, research questions, aims and methodology

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Masters at 70%
• For VCRS Awards, Masters at 75%
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications - peer-reviewed
• Experience of presentation of research findings

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

This project is funded by:

• BBSRC/UKRI Doctoral Landscape Award

This fully funded PhD scholarship will cover tuition fees and provide a maintenance allowance of £20,780 per annum for four years* (subject to satisfactory academic performance).  A Research Training Support Grant (RTSG) of £5000 per annum is also available.

This scholarship is open to applicants worldwide, regardless of residency or domicile.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

*Part time PhD scholarships are available, based on 0.5 of the full time rate.

Due consideration should be given to financing your studies.