Center for Precision Surgery, Houston Methodist Research Institute, Houston, TX, USA; LaSIE, UMR 7356 CNRS, La Rochelle Université, La Rochelle, France; Department of Surgery, Houston Methodist Hospital, Houston, TX, USA.
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
Exp Neurol. 2024 Dec;382:114980. doi: 10.1016/j.expneurol.2024.114980. Epub 2024 Sep 29.
Artificial intelligence (AI) has the potential to revolutionize chronic pain management by guiding the development of effective treatment strategies that are tailored to individual patient needs. This potential comes from AI's ability to analyze large and heterogeneous datasets to identify hidden patterns. When applied to clinical datasets of a particular patient population, AI can be used to identify pain subtypes among patients, predict treatment responses, and guide the clinical decision-making process. However, integrating AI into the clinical practice requires overcoming challenges such as data quality, the complexity of human pain physiology, and validation against diverse patient populations. Targeted, collaborative efforts among clinicians, researchers, and AI specialists will be needed to maximize AI's capabilities and advance current management and treatment of chronic pain conditions.
人工智能（AI）有可能通过指导制定针对个体患者需求的有效治疗策略来彻底改变慢性疼痛管理。这种潜力来自于 AI 分析大型和异构数据集以识别隐藏模式的能力。当将 AI 应用于特定患者群体的临床数据集时，它可以用于识别患者中的疼痛亚型，预测治疗反应，并指导临床决策过程。然而，将 AI 整合到临床实践中需要克服数据质量、人类疼痛生理学的复杂性以及针对不同患者群体进行验证等挑战。需要临床医生、研究人员和 AI 专家之间进行有针对性的合作，以最大限度地发挥 AI 的能力，并推进当前对慢性疼痛状况的管理和治疗。
