Dr Kieran Zucker

Profile

I completed my BSc in Medical Sciences with Molecular Medicine at University College London (UCL) in 2009 undertaking research into the mRNA expression of Flavin Containing Monooxygenase knockout mice. I continued at UCL to complete my undergraduate medical degree in 2012.

During my time at UCL I worked as a research associate at the Centre for Evidence at Transplantation based at the Royal College of Surgeons conducting research into the rate of publication of randomised controlled trials in transplant medicine. I also undertook a Society for General Microbiology Studentship characterising the enzymatic properties of a previously unstudied bacteria implicated in cases of endocarditis.

I undertook my Foundation Training and Core Medical training in the Yorkshire region before being promoted early to the role of clinical oncology specialist registrar in 2016. During this time I was involved in a number of clinical research projects and had regular input in the management of patients enrolled in clinical trials.

I took up my role as Clinical Research Fellow based at the Leeds Institute for Data Analytics in 2017 before completing my PhD in Health Data Science and Machine Learning.

In 2021 I took up my current role as an NIHR Clinical Lecturer working on a number of projects including cancer outcome prediction, computer vision, natural language processing, data visualisation, in silico trials and process mining.

I am a Fellow of the Faculty of Clinical Informatics where I am an elected member of council, have co-founded the Early Career Group and sit on the steering group for the Faculty’s Artificial Intelligence Specialist Interest Group. I am also a member of Royal College of Radiologists Clinical Oncology Artificial Intelligence Advisory Group.

Responsibilities

• Lead for Foundation Program in Medical Data Science and AI
• Centre for Doctoral Training In Medical Artificial Intelligence Supervisor
• Center for Computational Imaging & Simulation Technologies in Biomedicine: Core Clinical Academic

Research interests

My main area of interest is in translational data science and artificial intelligence. My work aims to make use of clinical data to develop tools with real world applicability and deliver their safe implementation into clinical settings. My work therefore focusses on a large number of elements from the data science and machine learning development pipeline including data quality and provenance, algorithmic fairness and bias, interpretability, validation and in silico trials.

I head up the comorbidity and late effects workstream of the Comprehensive Patient Records Project (CPR). This work aims to identify how pre-existing health conditions impact on outcomes in the 20 most common cancer and how cancer and its treatment impacts the long term health of patients. The research focusses on the analysis of large volumes of routinely collected health data including a linked dataset combining both primary and secondary care data. I have a particular interest in artificial intelligence methods and uses both traditional statistical approaches and machine learning approaches. My previous work has focussed on hospital only data however the CPR project seeks to use similar approaches to linked primary and secondary care data.

I am also the creator and lead developer of AuguR, a cancer analytics web application allowing clinicians to interact with real world clinical data relating to oncology. This software is currently being rolled out across the Yorkshire and Humber Region. I also provide support to a range of other interdisciplinary projects across the university and in my role as NIHR clinical lecturer provide integrated data science and clinical input to several healthcare artificial intelligence researchers in the computer sciences department. Projects include automated image analytics for the diagnosis of Covid-19 from chest x-rays, automated reporting of cardiac MRI, process mining healthcare data, the development of interactive clinical outcomes dashboards, BRCA and breast cancer outcomes and health geography projects.

• The Digital Health Hive

Qualifications

• PhD
• MBBS
• BSc (Hons)

Professional memberships

• Fellow of the Faculty of Clinical Informatics
• Member of the Royal College of Physicians

Student education

I designed and am the lead for the UK’s first accredited Foundation Program in Medical Data Science and Machine Learning Engineering which commences in 2023. I am also involved as a supervisor and the implementation of AI Clinical Fellowship posts in Yorkshire for Clinical Oncologists.

I am involved in a further range of educational activities across the university. I have been involved in the teaching of undergraduate medicals students since 2012 and has provided both formal and informal teaching in clinical medicine. My work with undergraduate medical students now predominantly focusses on Oncology related teaching. I also act as a PhD supervisor, MSc supervisor, MRES and LIDA data science intern supervisor. I contribute to supervision and teaching on the following programmes:

• Center for Doctoral Training In Medical Artificial Intelligence
• LIDA Data Science Internship Scheme
• MBBS Medicine
• Postgraduate Foundation Programme (Yorkshire and Humber Deanery)
• Postgraduate Internal Medicine Training Programme (Yorkshire and Humber Deanery)

I also increasingly deliver focussed teaching to clinical academics on data science with a particular focus on best practice approaches to coding.

Outside of the University I work with the Royal College of Radiologists and the Faculty of Clinical Informatics in the development of data science and machine learning educational materials.

Research groups and institutes

• Leeds Institute of Medical Research at St James's