Agentic artificial intelligence systems for dynamic health management and real-time genomic data analysis
Recently, rapid advancement toward intelligent health monitoring and management for individuals and populations has become possible with novel sensor technologies and miniaturized health data analysis tools. These developments in combination with growing interest in genetic and epigenetic factors associated with disease have accelerated the realization of the goal of integrating dynamic health status and its interaction with genomic data to improve surveillance and real-time prediction of disease risk or onset specifically for the individuals in need, and the identification of optimal interventions using the least invasive means. This review discusses essential computational data analysis and modeling techniques and tools that allow for the development of Agentic Artificial Intelligence Systems that feature frontline ability to perform proactive individualized real-time threat detection, prediction, and optimal intervention strategy identification including personalized behavioral modifications. New capabilities of multi-sensor signal fusion and multimodal biological and activity states assessment via low power wireless biomedical sensor networks and microelectromechanical systems are presented. The cyber-physical system concept is introduced to integrate microsystems for individuals with bioinspired methodology for population-level function modeling of the mutual influence of local epidemic outbreaks and global population mobility on pandemic outbreaks. A new experimental testing concept is described for emerging capabilities validation. The significance of agent-based and related multi-resolution modeling of the mutually interdependent behavioral evolution of individuals and dynamic disease status of communities and populations is emphasized. Specific applications include the management of chronic diseases, effect of subclinical factors on cancer and its complications, the role of infective agents in the emergence of latent diseases in compromised individuals and in acute disease exacerbations.
Institutions
- Natera (United States) (US)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2024-12-23
- DOI
- https://doi.org/10.5281/zenodo.15876658
- Primary Topic
- Heart Rate Variability and Autonomic Control
- Type
- article
- Field-Weighted Citation Impact
- 0.00