Yantao Xing, Kaiyuan Yang, Albert Lu, Ken Mackie, Feng Guo
{"title":"Sensors and Devices Guided by Artificial Intelligence for Personalized Pain Medicine.","authors":"Yantao Xing, Kaiyuan Yang, Albert Lu, Ken Mackie, Feng Guo","doi":"10.34133/cbsystems.0160","DOIUrl":null,"url":null,"abstract":"<p><p>Personalized pain medicine aims to tailor pain treatment strategies for the specific needs and characteristics of an individual patient, holding the potential for improving treatment outcomes, reducing side effects, and enhancing patient satisfaction. Despite existing pain markers and treatments, challenges remain in understanding, detecting, and treating complex pain conditions. Here, we review recent engineering efforts in developing various sensors and devices for addressing challenges in the personalized treatment of pain. We summarize the basics of pain pathology and introduce various sensors and devices for pain monitoring, assessment, and relief. We also discuss advancements taking advantage of rapidly developing medical artificial intelligence (AI), such as AI-based analgesia devices, wearable sensors, and healthcare systems. We believe that these innovative technologies may lead to more precise and responsive personalized medicine, greatly improved patient quality of life, increased efficiency of medical systems, and reducing the incidence of addiction and substance use disorders.</p>","PeriodicalId":72764,"journal":{"name":"Cyborg and bionic systems (Washington, D.C.)","volume":null,"pages":null},"PeriodicalIF":10.5000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11395709/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cyborg and bionic systems (Washington, D.C.)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.34133/cbsystems.0160","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Personalized pain medicine aims to tailor pain treatment strategies for the specific needs and characteristics of an individual patient, holding the potential for improving treatment outcomes, reducing side effects, and enhancing patient satisfaction. Despite existing pain markers and treatments, challenges remain in understanding, detecting, and treating complex pain conditions. Here, we review recent engineering efforts in developing various sensors and devices for addressing challenges in the personalized treatment of pain. We summarize the basics of pain pathology and introduce various sensors and devices for pain monitoring, assessment, and relief. We also discuss advancements taking advantage of rapidly developing medical artificial intelligence (AI), such as AI-based analgesia devices, wearable sensors, and healthcare systems. We believe that these innovative technologies may lead to more precise and responsive personalized medicine, greatly improved patient quality of life, increased efficiency of medical systems, and reducing the incidence of addiction and substance use disorders.
个性化疼痛医学旨在根据个体患者的具体需求和特征定制疼痛治疗策略,从而有望改善治疗效果、减少副作用并提高患者满意度。尽管已有疼痛标记物和治疗方法,但在理解、检测和治疗复杂疼痛状况方面仍存在挑战。在此,我们回顾了最近在开发各种传感器和设备以应对个性化疼痛治疗挑战方面所做的工程努力。我们总结了疼痛病理学的基本原理,并介绍了用于疼痛监测、评估和缓解的各种传感器和设备。我们还讨论了利用快速发展的医疗人工智能(AI)取得的进展,如基于 AI 的镇痛设备、可穿戴传感器和医疗保健系统。我们相信,这些创新技术可能会带来更精确、反应更迅速的个性化医疗,大大改善患者的生活质量,提高医疗系统的效率,并降低成瘾和药物使用障碍的发病率。