Research Supervisor Connect
This project aims to develop an AI-powered clinical trial recruitment system using large language models (LLMs) to match patients with suitable trials more effectively. The research focuses on leveraging generative AI to improve recruitment efficiency, accuracy, and accessibility in healthcare.
Electrical and Computer Engineering
The project seeks to address one of the most critical challenges in medical research: efficient and accurate patient recruitment. Current recruitment processes are often slow, costly, and hindered by mismatches between eligibility criteria and patient data. This research will develop an intelligent system that harnesses the power of LLMs to automatically interpret clinical trial protocols, extract inclusion and exclusion criteria, and match them with patient health records and medical histories. Generative AI will also be used to create patient-friendly trial descriptions, enhance communication, and support multilingual accessibility. The project will explore issues of data privacy, fairness, and bias mitigation to ensure ethical deployment in healthcare. The outcomes will contribute to faster, more inclusive, and cost-effective clinical trial recruitment, ultimately accelerating medical innovation and improving patient access to experimental treatments.
Research Techniques/Methodologies/Technologies: The research will employ large language models (LLMs), natural language processing (NLP), knowledge graphs, and generative AI techniques. Integration with electronic health record (EHR) systems, data anonymisation, and privacy-preserving AI methods will be key components.
Successful candidates:
Candidates should have a strong background in computer science, data science, biomedical informatics, or a related field. Proficiency in AI/ML (particularly NLP and LLMs), programming (Python, PyTorch, TensorFlow), and experience with healthcare datasets is desirable. Knowledge of clinical trial protocols, ethical AI, or health informatics will be advantageous. The candidate should have strong analytical and problem-solving skills and a keen interest in interdisciplinary research bridging AI and healthcare.
How to apply:
To apply, please email [email protected] the following:
The opportunity ID for this research opportunity is 3694