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Review Article | Open Access

Sensors and Devices Guided by Artificial Intelligence for Personalized Pain Medicine

Yantao Xing1,Kaiyuan Yang1,Albert Lu1,2Ken Mackie3Feng Guo1( )
Department of Intelligent Systems Engineering, Indiana University Bloomington, Bloomington, IN 47405, USA
Culver Academies High School, Culver, IN 46511, USA
Gill Center for Biomolecular Science, Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN 47405, USA

†These authors contributed equally to this work.

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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.

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Cyborg and Bionic Systems
Article number: 0160

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Cite this article:
Xing Y, Yang K, Lu A, et al. Sensors and Devices Guided by Artificial Intelligence for Personalized Pain Medicine. Cyborg and Bionic Systems, 2024, 5: 0160. https://doi.org/10.34133/cbsystems.0160

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Received: 08 June 2024
Revised: 01 August 2024
Accepted: 14 August 2024
Published: 13 September 2024
© 2024 Yantao Xing et al. Exclusive licensee Beijing Institute of Technology Press. No claim to original U.S. Government Works.

Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0).