Artificial Intelligence for musculoskeletal global health

PROJECT OVERVIEW

The World Health Organisation estimates musculoskeletal conditions to affect nearly a quarter of the global population. It impacts health outcomes and quality of life, and is in turn compounded by underlying intersectional health inequalities and wider determinants of health. At the interdisciplinary Planetary Health Informatics Lab  at NDORMS we study how artificial intelligence (AI) and large  health datasets including socio-geo-demographic, environmental, and wider exposome data can be used to generate evidence  and technological solutions for equitable musculoskeletal and orthopaedic health. Large routinely collected observational health data, coupled robust state-of-the-art artificial intelligence approaches can fill critical evidence gaps, generate diagnostic tools, and support more informed healthcare and policy decisions, ultimately improving health outcomes for everyone.

The successful candidate will have access to real world data such as electronic medical records, imaging, radiology, gait lab data, and wider data on social determinants of health (e.g. UK Biobank, CPRD) and other international databases. They will have an opportunity to use epidemiological, statistical, and artificial intelligence methods to undertake this study using multi-modality data coupled with foundation modelling generative AI approaches.  Our team has studied electronic health records for over 250 ethnicity groups from >60 million individuals in the UK. We have examined international climate and health trends over the last 30 years using earth observation data.

This DPhil project will bring together a variety of health datasets to generate evidence from real world data to describe, analyse and develop musculoskeletal-health prediction models for most at-risk populations. The project can involve a variety of data curation, descriptive analysis, and prediction modelling and forecasting techniques. Questions that may be of interest can include but not be limited to: 1) what are the impacts of wider determinants of health on musculoskeletal conditions and adverse health outcomes, 2) who is most at risk and how does risk vary, and 3) how can we develop early detection tools using multi-modality data sources. To achieve this, the student will have access to a range of secure data sources including but not limited to Clinical Practice Research Datalink (CPRD), Health Data Research UK (HDRUK) Trusted Research Environment, UKBiobank, international health datasets, bespoke gait analysis data and collect own data from the world renown Nuffield Orthopaedic Centre.

The student will be expected to bring their own ideas to enrich the study.

This is a 3 year DPhil project.

TRAINING OPPORTUNITIES

The PhD student will receive training, support and supervision from academic supervisors in a top world class academic environment at the Botnar Health Data Sciences and  Centre for Statistics in Medicine and the NDORMS at the University of Oxford. The student will be part of an established research group, the Planetary Health Informatics Lab with access to data, state-of-the-art high-performance computational resources and facilities, experience in the proposed research. They will join an exciting group of over >20 staff and 10 PhD and MSc students.

The group will ensure hands-on training in real world data science and artificial intelligence using health and environment data. The student will work on their unique project within an experienced and collaborative supervisory team. The student would be supported to attend relevant conferences to enrich their studies and financial support will be made available for travel to conferences.

Alongside with the opportunities listed above, the department offers extra training:

NDORMS  comprises three world-leading research institutes: the Botnar Institute for Musculoskeletal Sciences, the Kennedy Institute for Rheumatology and the Kadoorie Centre. The largest European academic department in its field, NDORMS is part of the Medical Sciences Division of the University of Oxford. We run a multi-disciplinary programme of research and teaching, supported by a grant portfolio worth over £230m.

The Centre for Statistics in Medicine is committed to improving the standard of health research through research and training on research and methods development, and home to the UK Equator Centre. This enables and encourages research and education to champion transparent and complete reporting of health research through reporting guidelines and training provision. A core curriculum of lectures will be taken to provide a solid foundation in a broad range of subjects including statistics, epidemiology, and big data analysis. All students will be required to attend a 2-day Statistical and Experimental Design course at NDORMS and the Real World Epidemiology: Oxford Summer School.  Students will also be required to attend regular seminars within the Department and have access to a variety of other courses run by the Medical Sciences Division Skills Training Team and the wider University, such as the UK Equator Publication School. Finally, the student(s) will be expected to regularly present data in Departmental seminars, collaborators across the University and internationally.

KEY READING

https://www.who.int/news-room/fact-sheets/detail/musculoskeletal-conditions

https://pmc.ncbi.nlm.nih.gov/articles/PMC10629784/

https://www.sciencedirect.com/science/article/pii/S0968016024002126

https://pubmed.ncbi.nlm.nih.gov/35348381/

KEYWORDS (5 WORDS MAXIMUM)

Real world data, musculoskeletal health, artificial intelligence, health equity

HOW TO APPLY

Please contact the relevant supervisor(s), to register your interest in the project, and, if required, the departmental Education Team (graduate.studies@ndorms.ox.ac.uk), who will be able to advise you of the essential requirements for the programme and provide further information on how to make an official application.

Interested applicants should have, or expect to obtain, a first or upper second-class BSc degree or equivalent in a relevant subject and will also need to provide evidence of English language competence (where applicable). The application guide and form is found online and the DPhil will commence in October 2026.

Applications should be made to the following programme using the specified course code.

D.Phil in Clinical Epidemiology and Medical Statistics (course code: RD_NNRA1)

For further information, please visit http://www.ox.ac.uk/admissions/graduate/applying-to-oxford.