Autonomous General Intelligence (AGI)-Inspired Framework for Unified Healthcare Decision Intelligence

Abstract

The growing complexity of modern healthcare systems requires intelligent solutions capable of integrating diverse data sources and performing adaptive decision-making across multiple domains. This paper presents an Autonomous General Intelligence (AGI)-Inspired Framework for Unified Healthcare Decision Intelligence. The proposed system aims to simulate human-like reasoning by combining multi-modal learning, knowledge representation, and adaptive learning mechanisms within a unified architecture. It integrates data from medical imaging, electronic health records, wearable devices, and clinical text to build a comprehensive understanding of patient health. Advanced deep learning models are combined with symbolic reasoning and reinforcement learning to enable continuous learning and dynamic decision-making. The framework incorporates self-learning capabilities, allowing it to adapt to new diseases, evolving medical knowledge, and patient-specific variations. Additionally, explainability and safety modules are included to ensure transparency and clinical reliability. Experimental evaluations demonstrate improved performance in disease prediction, treatment recommendation, and risk assessment compared to traditional AI models. Integration with prior research in healthcare analytics enhances system robustness and scalability. The study highlights the potential of AGI-inspired systems in transforming healthcare into an intelligent, adaptive, and fully integrated decision-making ecosystem.