South Shore Medical Center, Norwell, MA, USA.
Ecological and Evolutionary Signal-Processing and Informatics (EESI) Laboratory, Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA.
Curr Pain Headache Rep. 2024 Aug;28(8):769-784. doi: 10.1007/s11916-024-01264-0. Epub 2024 Jun 1.
This review aims to explore the interface between artificial intelligence (AI) and chronic pain, seeking to identify areas of focus for enhancing current treatments and yielding novel therapies.
In the United States, the prevalence of chronic pain is estimated to be upwards of 40%. Its impact extends to increased healthcare costs, reduced economic productivity, and strain on healthcare resources. Addressing this condition is particularly challenging due to its complexity and the significant variability in how patients respond to treatment. Current options often struggle to provide long-term relief, with their benefits rarely outweighing the risks, such as dependency or other side effects. Currently, AI has impacted four key areas of chronic pain treatment and research: (1) predicting outcomes based on clinical information; (2) extracting features from text, specifically clinical notes; (3) modeling 'omic data to identify meaningful patient subgroups with potential for personalized treatments and improved understanding of disease processes; and (4) disentangling complex neuronal signals responsible for pain, which current therapies attempt to modulate. As AI advances, leveraging state-of-the-art architectures will be essential for improving chronic pain treatment. Current efforts aim to extract meaningful representations from complex data, paving the way for personalized medicine. The identification of unique patient subgroups should reveal targets for tailored chronic pain treatments. Moreover, enhancing current treatment approaches is achievable by gaining a more profound understanding of patient physiology and responses. This can be realized by leveraging AI on the increasing volume of data linked to chronic pain.
本篇综述旨在探讨人工智能（AI）与慢性疼痛之间的相互关系，旨在确定聚焦领域以增强现有治疗方法并产生新的治疗方法。
在美国，慢性疼痛的患病率估计高达 40%以上。它的影响不仅体现在增加医疗保健成本、降低经济生产力以及对医疗资源的压力上，还体现在其复杂性以及患者对治疗的反应存在显著差异。由于这些原因，治疗这种疾病极具挑战性。目前的治疗方法往往难以提供长期缓解，其益处很少超过风险，例如依赖或其他副作用。目前，AI 已经影响了慢性疼痛治疗和研究的四个关键领域：（1）根据临床信息预测结果；（2）从文本中提取特征，特别是临床记录；（3）对“组学”数据进行建模，以识别具有潜在个性化治疗和改善疾病过程理解的有意义的患者亚组；（4）分离负责疼痛的复杂神经元信号，而目前的治疗方法试图调节这些信号。随着 AI 的发展，利用最先进的架构对于改善慢性疼痛治疗至关重要。目前的努力旨在从复杂数据中提取有意义的表示，为个性化医学铺平道路。确定独特的患者亚组应揭示针对特定慢性疼痛治疗的目标。此外，通过深入了解患者的生理和反应，还可以增强现有的治疗方法。这可以通过利用 AI 处理与慢性疼痛相关的不断增加的数据量来实现。
