NHS – Royal Free London 1

Innovation Placement 1 with the NHS

In collaboration with the NHS, DiRAC is pleased to invite applications for a 6-month Innovation Placement focussing on The Application of AI in Cancer Detection Rates

Host organisation: Royal Free London (RFL) NHS Foundation Trust
Location: IIA operates a hybrid working policy, with up to two days a week office based, however, fully remote would be considered with regular, pre-defined F2F meetings. As per RFL policy, employees cannot work outside the UK.  


More people than ever before are affected by cancer; an estimated 50% of people will receive a cancer diagnosis within their lifetime. Detecting cancer at an early stage saves lives. Early diagnosis is improving thanks to new research that addresses the needs of specific groups as well as screening and early detection. However, access times are poor and UK patients are often being diagnosed when their cancer is at a later stage than our European counterparts, making successful treatment less likely.

Cancer referrals and treatments at Royal Free London (RFL) have increased significantly in the last 12 years. The RFL receives over 50% of all urgent cancer referrals in North Central London (NCL).

The RFL is already leveraging AI and automation to improve cancer care. We run one of 15 breast screening services supporting the MIA (mammography intelligent assessment (MIA) programme to test if AI can improve breast cancer detection rates, and RFL dermatology is testing AI to address health inequalities and improve early diagnosis of skin cancer. 

The Project

The aim of this project is to provide insight into the impact of MRI scanning on outcomes in the breast cancer pathway. MRI scan slots are a scarce resource that is in high demand; unnecessary use of the MRI scanners causes delay in the care of other patients which causes significant system pressures given elective care waiting lists are currently very long. In addition, MRI scans, whilst not resulting in exposure to ionising radiation, are unpleasant and have the potential to cause harm for patients if incidental findings are picking up that need further investigation e.g. with invasive biopsies. By evaluating use of MRI in the breast cancer pathway RFL seeks to understand:

  • How are we currently using them? What types of patients (including their socio-economic status) and their clinical attributes are triggering an MRI scan, and how this is linked to a change in treatment plan, diagnosis and outcomes?
  • How do patient outcomes differ where MRIs have been performed and where they have not?
  • Is there unwarranted variation in the use of MRI?
  • What would be the impact on the wider hospital system if changes were made to the use of MRI in breast cancer?
  • How MRI scans are contributing toward changing health inequalities around access to care, and how we are affecting the patient experience and quality of life?

The project is designed to be accessible to candidates without healthcare data experience. Candidates will undertake training and shadowing sessions to understand how clinical data is generated and how errors can occur. The project will assess current practices, clean and organize data, and build a pipeline using data stewardship and data science. Statistical methods will be used to identify the target population and compare classical statistics approaches to machine learning. The project will also compare the impact of different data inputs and aims to produce work suitable for publication in peer-reviewed journals. The candidate will be expected to present their findings to both data and clinical groups across RFL, including:

  • Cancer clinical practice group
  • NCL cancer alliance
  • Data steering group

Candidate profile

Candidates will be expected to apply the tools and techniques they have learnt or used during their studies from areas with an advanced approach to data science, into an applied healthcare project.

A suitable candidate should have a good grounding in Python. Knowledge of random forest, data linkage and curation, and error propagation is desirable, but no essential.   

As this is project involves accessing NHS data, successful candidates will have to sign an honorary contract with RFL. 

This placement is now closed to applications.