Assessing the reliability of AI for predicting the risk of breast cancer

A 4-year, fully funded (tuition fees and stipend) project commencing in October 2022. This PhD studentship is delivered as part of the prestigious Industrial Cooperative Awards in Science & Technology (iCASE) EPSRC programme, and is a collaboration between the University of Surrey, the National Physical Laboratory (NPL) and the Royal Surrey NHS Foundation Trust (RSNFT).

Start date

1 October 2022


4 years

Application deadline

Funding source


Funding information

The studentship is fully funded by EPSRC iCASE voucher number 220022 and NPL top-up of £29,621

Stipend: £15,609/year plus living allowance top up of £2,991/year. Total: £18,600/year

The studentship also covers the tuition fee at the home rate (£4,500/year) and additional funds are available for training, and travel costs for visits to NPL, other project partners and academic conferences.

Funding for this project is available to UK students.

Supervised by


Improving early diagnosis of major diseases such as cancer and dementia is a top national healthcare priority. Improving screening programmes by making better use of data and digital technologies is a promising way to achieve this goal but there are questions around explainability and trustworthiness of automated screening decisions. A framework to assess the validity of such methods is needed to ensure they are reliable and of clinical use.

This project will develop a framework to assess the reliability of AI for predicting the risk of breast cancer. The OPTIMAM Database managed by the RSNFT contains data for over 150,000 women attending breast screening. The size of the database and the long-term longitudinal nature of this data resource make it ideally placed to aid in predicting risk of breast cancer. This project will use this data resource using AI applied to mammograms and other patient data such as age, breast density and ethnicity and, where possible, imaging data from other modalities such as MRI. The performance of an AI algorithm developed at RSNFT will be evaluated and compared with traditional models. It is critical that AI algorithms used in healthcare are reliable, explainable and not biased to any sub-group of the population. This project will investigate the effect of a variety of factors (e.g. image processing, measurements inferred from the image manufacturer, age and ethnicity) on the performance of the algorithm and to improve the generalisability.

This project will develop methodology to assess the generalisability of AI based tools applied to medical imaging. This requires the development of new knowledge and fundamental research in a way which is well-suited to a PhD project. The project brings together metrological analysis and data science skills with medical imaging, and involves working with specialists from several different disciplines, preparing the candidate for a career in either medical research or data-intensive metrology, and contributing to the growing need for trained experts in AI and big data in healthcare.

The project is a collaboration between the University of Surrey (Radiation and Medical Physics Group), the National Physical Laboratory (Data Science and Medical Physics) and the Royal Surrey NHS Foundation Trust (Scientific Computing). The student will be predominately based at the University of Surrey and RSNFT with possibility of placements at NPL for up to 3 months a year.

Related links

Data science at NPL Medical physics at NPL Scientific computing at Royal Surrey County Hospital OPTIMAM Mammography Imaging at Royal Surrey County Hospital

Main supervision team

Prof Rebecca Nutbrown, University of Surrey and Royal Surrey NHS Foundation Trust

Prof Phil Evans, University of Surrey and NPL

Dr Nadia Smith, NPL and Royal Surrey NHS Foundation Trust

Dr Matt Hall, NPL Medical Physics

Dr Mark Halling-Brown, Royal Surrey NHS Foundation Trust and Visiting Professor at the University of Surrey

Eligibility criteria

The studentship is available to UK candidates only. Applicants should hold a First or 2:1 UK honours degree in a relevant subject area, or a 2:2 alongside a good masters degree (a distinction is usually required).

If English is not your first language, you will be required to have an IELTS Academic of 6.5 or above (or equivalent), with no sub-test score below 6.

How to apply

Applications can be made through our Physics PhD course page.

Please state the project title and supervisor clearly on all applications. Please mention “EPSRC/NPL iCASE: AI for predicting risk of breast cancer” in your application.

Application deadline: 1 July 2022 or when suitable candidate is found

PhD Physics

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