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Purpose of Review <p>Neuromuscular blocking agents (NMBAs) are fundamental to modern anesthesia, yet residual neuromuscular blockade (rNMB) remains a significant cause of postoperative respiratory compromise. While quantitative neuromuscular monitoring (qNMM) mitigates this risk, it is inconsistently applied due to artifacts, device heterogeneity, and workflow constraints. This review synthesizes current developments in Artificial Intelligence (AI) and explores its potential to address these barriers by enhancing signal fidelity, predicting recovery trajectories, and supporting decision-making.</p> Recent Findings <p>Machine-learning techniques are increasingly applied to improve the reliability of neuromuscular monitoring. Significant progress has been made in signal-processing and automated outlier detection to filter artifacts from acceleromyographic and electromyographic data. Furthermore, hybrid models combining pharmacokinetic/pharmacodynamic (PK/PD) principles with machine learning are enabling more personalized predictions of recovery. Early prototypes for closed-loop systems demonstrate the feasibility of automated drug titration and objective reversal timing.</p> Summary <p>AI will not replace anesthesiologists but rather augment situational awareness and consistency in neuromuscular management. We outline a strategic roadmap emphasizing the need for high-quality datasets, prospective validation, and robust clinician education. The safe translation of these technologies demands transparency, multidisciplinary collaboration, and a continued focus on improving patient-centered outcomes.</p>

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Artificial Intelligence in Perioperative Neuromuscular Blockade Monitoring and Management: State-of-the-Art and Future Directions

  • 期刊论文

  • 发表日期：

    2026年7月29日

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  • H. Carvalho,
  • M. Verdonck

摘要

Purpose of Review

Neuromuscular blocking agents (NMBAs) are fundamental to modern anesthesia, yet residual neuromuscular blockade (rNMB) remains a significant cause of postoperative respiratory compromise. While quantitative neuromuscular monitoring (qNMM) mitigates this risk, it is inconsistently applied due to artifacts, device heterogeneity, and workflow constraints. This review synthesizes current developments in Artificial Intelligence (AI) and explores its potential to address these barriers by enhancing signal fidelity, predicting recovery trajectories, and supporting decision-making.

Recent Findings

Machine-learning techniques are increasingly applied to improve the reliability of neuromuscular monitoring. Significant progress has been made in signal-processing and automated outlier detection to filter artifacts from acceleromyographic and electromyographic data. Furthermore, hybrid models combining pharmacokinetic/pharmacodynamic (PK/PD) principles with machine learning are enabling more personalized predictions of recovery. Early prototypes for closed-loop systems demonstrate the feasibility of automated drug titration and objective reversal timing.

Summary

AI will not replace anesthesiologists but rather augment situational awareness and consistency in neuromuscular management. We outline a strategic roadmap emphasizing the need for high-quality datasets, prospective validation, and robust clinician education. The safe translation of these technologies demands transparency, multidisciplinary collaboration, and a continued focus on improving patient-centered outcomes.