A roadmap for artificial intelligence in pain medicine: current status, opportunities, and requirements.

IF 2.3 3区医学 Q2 ANESTHESIOLOGY

Current Opinion in Anesthesiology Pub Date : 2025-04-24 DOI:10.1097/ACO.0000000000001508

Meredith C B Adams, James S Bowness, Ariana M Nelson, Robert W Hurley, Samer Narouze

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引用次数: 0

Abstract

Purpose of review: 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.

Recent findings: 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.

Summary: 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.

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疼痛医学中人工智能的发展路线图：现状、机遇和需求。

综述目的：人工智能（AI）代表了疼痛医学的变革机遇，为疼痛评估和管理方面的长期挑战提供了潜在的解决方案。这篇综述综合了人工智能应用的现状和实施的战略框架，突出了来自邻近医学领域的已建立的适应途径。最近的研究发现：在急性疼痛中，人工智能系统已经获得了区域麻醉超声引导的监管批准，并显示出通过面部表情分析自动疼痛评分的前景。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，并通过预测建模增强了治疗选择。成功的整合需要跨学科合作和医生在整个开发过程中的共同领导，并需要针对特定的疼痛挑战进行特定的调整。摘要：本路线图概述了人工智能在疼痛医学中的综合方法框架，强调了四个关键阶段：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、实时手术指导和个性化治疗优化。成功最终取决于临床医生、开发人员和研究人员之间保持强有力的伙伴关系，同时解决伦理、监管和教育方面的问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Current Opinion in Anesthesiology 医学-麻醉学

CiteScore

4.90

自引率

8.00%

发文量

207

审稿时长

12 months

期刊介绍： Published bimonthly and offering a unique and wide ranging perspective on the key developments in the field, each issue of Current Opinion in Anesthesiology features hand-picked review articles from our team of expert editors. With fifteen disciplines published across the year – including cardiovascular anesthesiology, neuroanesthesia and pain medicine – every issue also contains annotated references detailing the merits of the most important papers.