NIHR Academic Clinical Fellow & Dissertation Lead, MSc Applied Digital Health
- NIHR Academic Clinical Fellow
- Medical Oncology Registrar, Oxford University Hospitals NHS Foundation Trust
- Dissertation Lead, MSc Applied Digital Health
I am an NIHR-funded Academic Clinical Fellow and Specialty Registrar in Medical Oncology at Oxford University Hospitals NHS Foundation Trust.
My research specialism is developing AI-enabled screening, diagnostic and prognostic tools based upon routinely collected healthcare data. Previously, I have led development and evaluation of an AI-enabled screening test for COVID-19 in emergency departments, based upon blood tests and vital signs recorded within 1h of a patient arriving in hospital. The CURIAL AI test was piloted in Oxford's John Radcliffe Hospital Emergency Department in early-2021, and described in an accompanying The Lancet Digital Health commentary as "an elegant breakthrough to enhance the clinical decision-making process in the age of artificial intelligence". More recently, I am studying applications of federated learning to develop and evaluate AI models without transferring patient data away from the premises of participating NHS hospital groups.
Alongside my clinical and research interests, I lead the 10-week practical dissertation module for the MSc in Applied Digital Health. The dissertation placements offer students an immersive practical experience and chance to develop a specialism within the digital health research and translational pipeline, while expanding their professional networks. Previous project placements have included opportunities to work with local hospitals, health informatics and engineering research groups, and startups within the Oxford ecosystem.
Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning
Yang J. et al, (2023), Nature Machine Intelligence
Scalable federated learning for emergency care using low cost microcomputing: Real-world, privacy preserving development and evaluation of a COVID-19 screening test in UK hospitals
Soltan AAS. et al, (2023)
An adversarial training framework for mitigating algorithmic biases in clinical machine learning
Yang J. et al, (2023), npj Digital Medicine, 6
Privacy-aware Early Detection of COVID-19 through Adversarial Training
ROHANIAN M. et al, (2023), IEEE journal of biomedical and health informatics