About the project
This PhD project is part of a cutting-edge research initiative aimed at developing transformative AI solutions for healthcare by leveraging big data to address key challenges in causal inference, continual learning, and digital twin technology.
This PhD project is designed to push the boundaries of artificial intelligence in healthcare, using state-of-the-art methods to analyse and extract insights from large and diverse datasets such as electronic health records, imaging data, and biochemical markers.
The goal is to improve patient care by developing AI models that not only predict clinical outcomes but can also adapt over time and provide causal insights to guide treatment decisions.
The project will focus on:
- Causal AI: applying causal inference techniques to understand underlying cause-and-effect relationships within complex medical data, supporting more accurate and interpretable AI models for healthcare applications.
- Continual Learning: developing machine learning models that evolve and adapt as new patient data becomes available, ensuring that the AI remains up-to-date and responsive to real-world changes.
- Digital Twin Modelling: creating digital twins—virtual patient simulations—to predict disease progression, optimise treatment plans, and enable personalised medicine at scale.
This research will contribute to developing robust and clinically valuable AI tools, enabling more precise and proactive healthcare.
