Interpretation and Implementation Prospects of the Expert Consensus on Clinical Application for Patient-controlled Analgesia Based on Intelligent Analgesia Technology - Shouzhang She; Bin Zheng; Hanzhong Cao; Qinjun Chu; Wenqi Huang; Weifeng Yu (International Journal of Pain Research, 2026)
The transition from conventional patient-controlled analgesia (PCA) to artificial?intelligence-assisted patient-controlled analgesia (Ai-PCA) represents an innovative model for analgesia management. Clinicians utilize AI-enabled PCA devices to deliver individualized analgesia according to patients’ pain intensity and physical status. This review interprets the 2024 Chinese Expert Consensus on Clinical Application of Patient-controlled Analgesia, a landmark document for innovative analgesic strategies. We summarize the historical evolution of postoperative PCA and Ai-PCA, the rationale for consensus development, clinical implementation details, quality management frameworks, and future directions of Ai-PCA. Measures to improve Ai-PCA performance and perioperative pain management are analyzed, highlighting priorities for further clinical translation. As a core clinical specialty reflecting comprehensive hospital service capacity, anesthesiology benefits greatly from artificial intelligence. Integrating AI into pain medicine ushers in an era of big-data-driven preventive analgesia, which can improve patient comfort and satisfaction, enhance enhanced recovery after surgery (ERAS), and guide clinicians toward standardized, rational pain therapy.