Available
Project number:
2025_74
Start date:
October 2025
Project themes:
Main supervisor:
Associate Professor in HealthCare AI
Co-supervisor:
Professor Richard Dobson
Additional Information:
Streamlining Access to Clinical Guidelines with Knowledge-Infused LLMs
Large language models (LLMs) have the potential to improve healthcare, particularly by delivering the right information to the right provider at the right time within hospital workflows. LLM-powered access to clinical knowledge could streamline the retrieval of clinical guidelines, significantly reducing the time doctors spend searching for information. However, despite impressive advancements, LLMs in healthcare still face major challenges, including difficulties in avoiding factual inaccuracies and limitations in clinical reasoning. Current approaches to guidelines Q&A explore strategies for cleaning and reformatting guidelines, combined with RAG (retrieval-augmented generation), which enhances the precision of LLM responses to some extent. Recently, RAG approaches that incorporate structured knowledge, such as knowledge graphs, have shown promise in further improving the reliability of responses. However, this solution introduces new challenges, including mapping ambiguous user queries to structured knowledge and selecting the relevant parts of the knowledge graph to address the query. The aim of the project is to develop a novel methodology to assist clinical decision making by retrieving relevant medical guidelines using LLMs. The concrete objectives include: (1) Developing a methodology for building knowledge graphs from medical guidelines using LLMs; (2) Developing a methodology to match expert queries to relevant parts of structured knowledge to produce reliable clinical support; (3) Develop a robust evaluation framework to ensure the validity of the generated clinical suggestions. Main project output will be a chatbot answering medical inquiries using the knowledge from medical guidelines.
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
