Anthony Hughes

Artificial intelligence (AI) has exploded into the mainstream in recent years, paving the way for the development of large language models (LLM) and large multimodal models (MM). These models are allowing for new and innovative ways of tackling societal problems. This increased use of LLMs and MMs is having a great influence in healthcare in many applications, including prediction of patient outcomes, medical decision making, and the potential to fuse and infer over a multitude of patient signals. Consequently, the critical and high-impact nature of this environment is fuelling the need for a model's ability to detect and adapt to adversarial behaviours over time.

Great strides have been made in incorporating LLMs into a healthcare setting. These sophisticated models now possess the ability to surpass human performance in tasks such as medical exams. However, LLM training processes that focus on generalising across a particular data distribution from a particular point in time are a cause for concern. This style of model training is static, consequently paving the way for models that could become misaligned with the environment in which they operate. In this project, we look to learn and build mechanisms for combating a model's misalignment, helping models to continually learn.

The multimodal nature of medicine requires the development of MMs in order to process and interpret multiple types of data. In collaboration with local healthcare organisations, this PhD project is aimed at harnessing the potential of LLMs and MMs in the medical domain to improve healthcare outcomes, enhance patient care, and assist healthcare professionals. It will in particular focus on how LLMs and MMs can be adopted to handle and interpret the highly granular data in medicine while ensuring the models remain robust to data changes and task changes over periods of time.