This research aims to develop and validate an integrated multimodal artificial intelligence (AI) framework for disease risk prediction and personalized healthcare using the UK Biobank dataset.
Research Questions:
1.Can combining multiple data modalities (e.g., genomics, clinical, imaging, lifestyle) improve disease risk prediction accuracy?
2.What machine learning and deep learning models are most effective for multimodal health data?
3.How can multimodal AI contribute to early diagnosis, patient stratification, and personalized treatment strategies?
Objectives:
1.To build a scalable AI framework that integrates diverse health data types.
2.To compare the performance of multimodal AI models with unimodal models.
3.To identify important predictors and biomarkers across modalities that are associated with specific health outcomes.
4.To explore the potential of AI-driven models in supporting clinical decision-making.
Scientific Rationale:
Traditional models relying on a single data type may miss complex patterns present across multiple biological and behavioral layers. Multimodal AI enables the fusion of heterogeneous data sources, providing a more comprehensive view of patient health. Leveraging UK Biobank’s rich dataset, this research will contribute to precision medicine by improving disease prediction, aiding early intervention, and advancing personalized care strategies.