Building artificial intelligence virtual tissue (AIVT) for tissue state representation, feature prediction, and dynamic simulation
This work addresses the need for advanced computational models in tissue biology, but it is a conceptual proposal without empirical validation.
The authors propose the concept of AI virtual tissue (AIVT), a framework using spatial multimodal data to model tissue states, enabling representation, feature prediction, and dynamic simulation. No concrete results are provided as this is a conceptual paper.
Modeling tissue states and their transitions is essential for understanding tissue homeostasis in health and pathological remodeling in disease. However, conventional computational modeling approaches are inadequate to capture the complexity of tissues as spatially organized, multiscale biological systems. Artificial intelligence (AI) has shown a remarkable ability for representing intricate systems, creating new opportunities to characterize tissue states and their transitions. Here, we propose the concept of AI virtual tissue (AIVT), an AI framework grounded in spatial multimodal data for modeling tissues in health and disease. AIVT is designed to learn unified, spatially resolved, and dynamically manipulatable representations of tissue state, enabling tissue state representation and analysis, molecular and morphological feature prediction, and simulation of spatiotemporal tissue dynamics. We outline the fundamental assumptions, core capabilities, architectural components, as well as data and algorithm foundations of AIVT as a framework for AI-driven tissue modeling.