Adams Meredith C B, Bowness James S, Nelson Ariana M, Hurley Robert W, Narouze Samer
Departments of Anesthesiology, Translational Neuroscience, and Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.
Pain Outcomes Lab, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.
Curr Opin Anaesthesiol. 2025 Apr 24. doi: 10.1097/ACO.0000000000001508.
Artificial intelligence (AI) represents a transformative opportunity for pain medicine, offering potential solutions to longstanding challenges in pain assessment and management. This review synthesizes the current state of AI applications with a strategic framework for implementation, highlighting established adaptation pathways from adjacent medical fields.
In acute pain, AI systems have achieved regulatory approval for ultrasound guidance in regional anesthesia and shown promise in automated pain scoring through facial expression analysis. For chronic pain management, machine learning algorithms have improved diagnostic accuracy for musculoskeletal conditions and enhanced treatment selection through predictive modeling. Successful integration requires interdisciplinary collaboration and physician coleadership throughout the development process, with specific adaptations needed for pain-specific challenges.
This roadmap outlines a comprehensive methodological framework for AI in pain medicine, emphasizing four key phases: problem definition, algorithm development, validation, and implementation. Critical areas for future development include perioperative pain trajectory prediction, real-time procedural guidance, and personalized treatment optimization. Success ultimately depends on maintaining strong partnerships between clinicians, developers, and researchers while addressing ethical, regulatory, and educational considerations.
人工智能(AI)为疼痛医学带来了变革性机遇,有望解决疼痛评估和管理中长期存在的挑战。本综述综合了人工智能应用的现状以及实施的战略框架,突出了从相邻医学领域借鉴的既定适应途径。
在急性疼痛方面,人工智能系统已获得区域麻醉超声引导的监管批准,并在通过面部表情分析进行自动疼痛评分方面展现出前景。对于慢性疼痛管理,机器学习算法提高了肌肉骨骼疾病的诊断准确性,并通过预测建模优化了治疗选择。成功整合需要跨学科合作以及医生在整个开发过程中的共同领导,同时针对疼痛特有的挑战进行特定调整。
本路线图概述了人工智能在疼痛医学中的全面方法框架,强调四个关键阶段:问题定义、算法开发、验证和实施。未来发展的关键领域包括围手术期疼痛轨迹预测、实时操作指导和个性化治疗优化。成功最终取决于临床医生、开发者和研究人员之间保持紧密合作,同时解决伦理、监管和教育方面的问题。