Generative AI models in time varying biomedical data: a systematic review (Preprint)

Abstract

BACKGROUND Trajectory modeling is a longstanding challenge in the application of computational methods to healthcare. However, traditional statistical and machine learning methods do not achieve satisfactory results as they often fail to capture the complex underlying distributions of multi-modal health data, and long-term dependencies throughout patients’ medical histories. Recent advances in generative AI have provided powerful tools to represent complex distributions and patterns with minimal underlying assumptions. These have had a major impact in fields such as finance and environmental sciences, and recently researchers have turned to these methods for disease modeling. OBJECTIVE While AI methods have proven powerful, their application in clinical practice remains limited due to their black-box like nature. The proliferation of AI algorithms poses a significant challenge for non-developers to track and incorporate these advances into clinical research and application. In this work, we survey peer-reviewed, generative AI model papers with specific applications in time series health data. METHODS Our search includes single- and multi-modal generative AI models that operate over structured and unstructured data, medical imaging or multi-omics data. We introduce current generative AI methods, review their applications in each data modality and discuss their strengths and weaknesses compared to traditional methods. RESULTS We follow the PRISMA guideline and review 155 articles on generative AI applications in time series healthcare data across modalities. Furthermore, we offer a systematic framework for clinicians to easily identify suitable AI methods for their data and task at hand. CONCLUSIONS We review and critique existing applications of generative AI to time series health data with the aim of bridging the gap between computational methods and clinical application. We also identify shortcomings of existing approaches, and highlight recent advances in generative AI that represent promising directions for healthcare modeling.