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Fairness in AI for Cardiac Imaging

Subject areas:

Mathematics, Computer Science, Artificial Intelligence, Biomedical Engineering, Medical Imaging

Funding type:

Stipend. Tuition fee. Bench Fees / Research Training & Support Grant.

Awarding body:

The Engineering and Physical Sciences Research Council (EPSRC).



Investigating potential bias in AI models for cardiac image analysis with a focus on cardiac magnetic resonance imaging (MRI), as well as developing novel tools for mitigating bias.

Award details

The subject of ‘fairness’ in artificial intelligence (AI) is a relatively new but fast-growing research field. It refers to assessing AI algorithms for potential bias based on demographic characteristics such as race, and the development of algorithms to address this bias. With AI models starting to be deployed in the real world it is seen as essential that its benefits are shared equitably according to race, gender and other demographic characteristics, and so efforts to ensure fairness of deployed models have generated much interest and some controversy (e.g. https://www.bbc.co.uk/news/technology-55164324). Most applications to date have been in computer vision, although some work in healthcare has started to emerge.

AI-based quantification of cardiac structure and function is also an active research area, and the success of AI in this field has meant that it is currently moving towards wider clinical translation. At the same time, it has long been well understood that cardiac structure and function, as well as the mechanisms leading to cardiovascular disease, vary according to demographic characteristics such as gender and race. Therefore, it is surprising that no work to date has investigated potential bias in AI models for cardiac image analysis. The aim of this project is to investigate this possibility, with a focus on cardiac magnetic resonance imaging (MRI), as well as to develop novel tools for mitigating the bias, thus creating fairer tools for clinical use.

Candidates for this position are expected to have strong computational skills, experience in or a desire to learn about AI and machine learning as well as a commitment to advancing the fairness of AI algorithms in healthcare.

Award value

This EPSRC Doctoral Training Partnership (DTP) studentship is fully funded for 3.5 years. This includes home tuition fees, stipend and generous project consumables.

Stipend: Students will receive a tax-free stipend at the UKRI rate of ca £17,000 per year as a living allowance.

Research Training Support Grant (RTSG): A generous project allowance will be provided for research consumables and for attending UK and international conferences.

Award conditions

Eligibility criteria:

Candidates who meet the eligibility requirements for Home Fee status will be eligible to apply for this project. Home students will be eligible for a full UKRI award, including fees and stipend, if they satisfy the UKRI criteria below, including residency requirements. To be classed as a Home student, candidates must meet the following criteria:

  • be a UK National (meeting residency requirements), or
  • have settled status, or
  • have pre-settled status (meeting residency requirements), or
  • have indefinite leave to remain or enter.

 

 

Applicable level of study: Postgraduate research

Application process

Please submit an application for the Biomedical Engineering and Imaging Science Research MPhil/PhD (Full-time) programme using the King’s Apply system. Please include the following with your application:

  • A PDF copy of your CV should be uploaded to the Employment History section.
  • A 500-word personal statement outlining your motivation for undertaking postgraduate research should be uploaded to the Supporting statement section.
  • Funding information: Please choose Option 5 “I am applying for a funding award or scholarship administered by King’s College London” and under “Award Scheme Code or Name” enter BMEIS_DTP. Failing to include this code might result in you not being considered for this funding.

 

Please email:

Dr Andrew King andrew.king@kcl.ac.uk,

Dr Miaojing Shi miaojing.shi@kcl.ac.uk

 
 

Academic year:

2021-22

Study mode:

Postgraduate research

Application closing date:

15 May 2021