Artificial Intelligence with Robotics for Metabolic Rehabilitation and Enhanced Patient Recovery in Critical Care

This narrative review summarizes recent advances in the integration of artificial intelligence (AI)-driven rehabilitation robotics with metabolic regulation in critically ill pulmonary patients. AI-enabled robotic systems, combining multimodal physiological sensing with adaptive machine learning, allow continuous monitoring of cardiopulmonary and metabolic parameters and support individualized, dynamic interventions. Unlike conventional rehabilitation based on fixed protocols, these systems establish closed-loop feedback between metabolic signals and motor output, enabling sustained low-intensity muscle activation while optimizing oxygen utilization, glucose metabolism, and mitochondrial function. Such regulation may interrupt the pathological interplay among inflammation, metabolic imbalance, and muscle atrophy, thereby promoting respiratory and systemic recovery. Recent developments in metabolic monitoring, biofeedback control, and multi-omics integration have further extended these platforms toward comprehensive metabolic management. By integrating biomechanical support with computational and biochemical intelligence, this approach reframes rehabilitation as an active process of metabolic reprogramming. However, current evidence remains heterogeneous, and well-designed clinical studies are needed to validate the reproducibility and clinical efficacy of these strategies.