Using large language models to predict MS disease progression

The progression of disability in MS is complex and difficult to predict, particularly over short timeframes. There is still a lack of biological signs (biomarkers) that can accurately predict the progression of MS, which hinders the implementation of individualised MS treatment plans. An individualised approach could be adopted into clinical practice immediately, enabling early intervention with highly effective treatments in people who are at greatest risk of MS disease progression.

For this project, Dr Dongang Wang and his team will focus on improving how disease progression in MS can be predicted, using advanced artificial intelligence (AI) technology.

The main aims are to:

1. Combine magnetic resonance imaging (MRI) scans and deep learning technologies to develop tools for monitoring disease and predicting the likelihood of progression.
2. Explore how cutting-edge large machine learning models (programs that learn from data to make decisions or predictions) can identify key clinical predictors of disease progression.
3. Evaluate the effectiveness of these tools in a real-world study using data from multiple centres.

By integrating cutting-edge AI and data from MSBase, the largest international MS registry consisting of clinical data, Dr Wang and his team aim to improve the precision with which clinicians can predict MS progression in individuals.

Ultimately, Dr Wang will develop a tool that can be easily implemented to facilitate early tailored treatment and improve long-term clinical outcomes.