Towards a Unified Framework for Cross Domain AI: Comparative Insights from Healthcare, Robotics, and Knowledge Systems

Abstract

Artificial intelligence (AI) has progressed rapidly in healthcare, robotics, and knowledge-based reasoning; however, these domains have developed largely in isolation, limiting cross-domain knowledge transfer and integrative cognitive capabilities. While each field employs distinct methodologies, they rely on shared foundational processes, including abstraction, multimodal integration, semantic representation and adaptive decision-making. The absence of a unifying theoretical structure constrains the development of AI systems capable of generalizing across heterogeneous environments while maintaining their interpretability and reliability. This study conducts a comparative conceptual analysis of healthcare AI, embodied robotics, and symbolic knowledge systems to identify structural commonalities across the perception, representation, reasoning, and decision-making processes. Through cross-domain representational mapping, the findings reveal convergent layered architectures that integrate statistical learning and structured semantic reasoning. Despite differences in operational constraints, such as safety, real-time processing, and logical precision, the domains share core cognitive functions that support alignment. Based on this synthesis, this study proposes a four-layer unified cognitive architecture comprising perceptual grounding, symbolic abstraction, cognitive integration, and adaptive decision-making. The framework offers a theoretical foundation for designing AI systems capable of cross-domain generalization while preserving semantic coherence and interpretability.

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