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Sensors and Devices Guided by Artificial Intelligence for Personalized Pain Medicine.
Xing, Yantao; Yang, Kaiyuan; Lu, Albert; Mackie, Ken; Guo, Feng.
Affiliation
  • Xing Y; Department of Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN 47405, USA.
  • Yang K; Department of Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN 47405, USA.
  • Lu A; Department of Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN 47405, USA.
  • Mackie K; Culver Academies High School, Culver, IN 46511, USA.
  • Guo F; Gill Center for Biomolecular Science, Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN 47405, USA.
Cyborg Bionic Syst ; 5: 0160, 2024.
Article in En | MEDLINE | ID: mdl-39282019
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.

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Cyborg Bionic Syst Year: 2024 Document type: Article Affiliation country: United States Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Cyborg Bionic Syst Year: 2024 Document type: Article Affiliation country: United States Country of publication: United States