A Hindsight-Insight-Foresight Analysis of AI in Mission-Critical Healthcare Information Systems

초록

The convergence of Artificial Intelligence (AI) and Mission-Critical Systems (MCS) has transformed healthcare into an intelligent, interconnected, and adaptive environment. This paper presents a concise analysis of AI integration into mission-critical healthcare information systems through a threefold framework: Hindsight, Insight, and Foresight. Hindsight traces early symbolic and statistical AI systems such as MYCIN and CADIAG, which laid the foundation for clinical decision support. Insight highlights recent advances, including deep learning for perception, reinforcement learning for adaptive decision-making, explainable AI (XAI) for interpretability, and federated learning (FL) for privacy-preserving collaboration. Foresight explores the emergence of Generative and Agentic AI as enablers of real-time, distributed, and trustworthy healthcare operations within 6G-enabled environments. A compact taxonomy is proposed to classify AI approaches in healthcare mission-critical systems based on technique, system layer, and safety function. This synthesis provides a roadmap for developing robust, explainable, and privacy-preserving AI solutions aligned with computing and telecommunications convergence.

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