Available
Project number:
2025_02
Start date:
October 2025
Project themes:
Main supervisor:
Senior Lecturer in AI for Medicine
Co-supervisor:
Additional Information:
Towards Trustable Healthcare Generative AI Models
Background: The rapid advancements in large language models (LLMs) have led to significant interest in applying these technologies to clinical decision support. LLMs have shown impressive capabilities in natural language generation. However, their integration into clinical environments poses grand challenges due to issues with organising temporal patient information, accuracy, hallucination prevention and adherence to common-sense and clinical guidelines. Logic-based systems, on the other hand, are known for their precision and ability to encode complex rules and reasoning processes. Aims and Objectives Building on the in-house developed clinical large language model, Foresight [1], this project explores different modes of integration of LLMs with logical paradigms to leverage the strengths of both approaches in a clinical setting, thereby improving soundness, explainability, and trust. The specific objectives are: Equip Foresight with a robust temporal representation of patient data including diagnoses, symptoms and frequent measurements. Develop and evaluate a validation framework of an LLM's output, quantifying the reliability of the recommendations generated. Build on the formalisms developed to produce high-level explanations of the produced recommendations. Clinical Application of the developed frameworks to complex scenarios focusing on established use cases within the supervisor's research group including deterioration detection and diagnostics within hospital settings, including our local King's College Hospital. Novelty In addition to methodological novelty whose impact in AI research is timely, this project's importance is underscored by the potential for the inception of the first trustworthy healthcare LLM, with potential to improve patient outcomes and set a new standard for AI applications in healthcare. [1] Kraljevic, Z., Bean, D., Shek, A., Bendayan, R., Yeung, J. A., Deng, A., Baston, A., Ross, J., Idowu, E., Teo, J. T., & Dobson, R. J. (2022). Foresight -- Deep Generative Modelling of Patient Timelines using Electronic Health Records.
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 30 January 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 March 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
