Available
Project number:
2025_01
Start date:
October 2025
Project themes:
Main supervisor:
Senior Lecturer in AI for Medicine
Co-supervisor:
Additional Information:
From Multimodality to Insight - Beyond GenAI in Medicine
Medical data from Electronic Health Records (EHRs) includes complex, time-variant signals such as images, text, and structured measurements. While various deep learning architectures have been developed to process and analyse these multimodal signals for medical knowledge discovery and clinical decision-making, they face significant challenges:
1. The complexity and diversity of the data make it difficult to create a single model or architecture capable of handling all signal types.
2. Deep learning models require large, consistent data streams, which are often unavailable in hospital settings where monitoring is intermittent and inconsistent. This variability in data sampling frequencies hinders the development of robust architectures.
3. Current models often fail to derive insights from out-of-distribution data, crucial for accurately assessing a patient's condition and addressing biases and inaccuracies. Insights must be meaningful, accurate, and compliant with existing medical knowledge, guidelines, and procedures.
Project Aims & Objectives
This project addresses the key issues obstructing the effective utilisation of medical signals from EHRs. The outputs of the project will be a series of novel healthcare predictive and prescriptive models combining neural architectures and logical reasoning to achieve the following:
1. Investigating methodologies to integrate medical guidelines and constraints with deep learning models. This will also contribute to interpretability frameworks to mitigate the opacity of deep models. Potential approaches include logical neural networks, graph neural networks, or designing a logical engine that interacts with the neural network.
2. Examining different approaches to applying generative models to various EHR-derived data modes, including textual data.
We are now accepting applications for 1 October 2025
How to apply
Candidates should possess or be expected to achieve a 1st or upper 2nd class degree in a relevant subject including the biosciences, computer science, mathematics, statistics, data science, chemistry, physics, and be enthusiastic about combining their expertise with other disciplines in the field of healthcare.
Important information for International Students:
It is the responsibility of the student to apply for their Student Visa. Please note that the EPSRC DRIVE-Health studentship does not cover the visa application fees or the Immigration Health Surcharge (IHS) required for access to the National Health Service. The IHS is mandatory for anyone entering the UK on a Student Visa and is currently £776 per year for each year of study. Further detail can be found under the International Students tab below.
Next Steps
- Applications submitted by the closing date of Thursday 6 February 2025 will be considered by the CDT. We will contact shortlisted applicants with information about this part of the recruitment process.
- Candidates will be invited to attend an interview. Interviews are projected to take place in April 2025.
- Project selection will be through a panel interview chaired by either Professor Richard Dobson and Professor Vasa Curcin (CDT Directors) followed by informal discussion with prospective supervisors.
- If you have any questions related to the specific project you are applying for, please contact the main supervisor of the project directly.
For any other questions about the recruitment process, please email us at