Matthew T. Flavin, Jose A. Foppiani, Marek A. Paul, Angelica H. Alvarez, Lacey Foster, Dominika Gavlasova, Haobo Ma, John A. Rogers, Samuel J. Lin
{"title":"Bioelectronics for targeted pain management","authors":"Matthew T. Flavin, Jose A. Foppiani, Marek A. Paul, Angelica H. Alvarez, Lacey Foster, Dominika Gavlasova, Haobo Ma, John A. Rogers, Samuel J. Lin","doi":"10.1038/s44287-025-00177-3","DOIUrl":null,"url":null,"abstract":"Pain management in humans is an unresolved problem with substantial medical, societal and economic implications. Traditional strategies such as opioid-based medications are highly effective but pose many long-term risks, including addiction and overdose. In this Review, we discuss these persistent challenges in medical care along with advances in bioelectronics that enable safer and more effective alternative treatments. Emerging approaches leverage wireless embedded networks and machine learning to accurately detect and quantify the symptoms of pain, establishing a foundation for targeted, on-demand treatment. These platforms offer a powerful complement to wearable and implantable neural interfaces that can control these symptoms with unprecedented spatiotemporal and functional selectivity. Now, emotional and cognitive aspects of pain can be addressed through immersive multisensory engagement with systems for augmented and virtual reality. Trends in diagnostic and interventional technologies show how their integration is well suited to addressing some of the most intractable problems in pain management. Pain is a profound and unresolved health challenge, and current interventions are not sufficient for safe and effective pain management. Bioelectronics presents solutions for monitoring the symptoms of pain and treating these symptoms in a targeted manner.","PeriodicalId":501701,"journal":{"name":"Nature Reviews Electrical Engineering","volume":"2 6","pages":"407-424"},"PeriodicalIF":0.0000,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Reviews Electrical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s44287-025-00177-3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Pain management in humans is an unresolved problem with substantial medical, societal and economic implications. Traditional strategies such as opioid-based medications are highly effective but pose many long-term risks, including addiction and overdose. In this Review, we discuss these persistent challenges in medical care along with advances in bioelectronics that enable safer and more effective alternative treatments. Emerging approaches leverage wireless embedded networks and machine learning to accurately detect and quantify the symptoms of pain, establishing a foundation for targeted, on-demand treatment. These platforms offer a powerful complement to wearable and implantable neural interfaces that can control these symptoms with unprecedented spatiotemporal and functional selectivity. Now, emotional and cognitive aspects of pain can be addressed through immersive multisensory engagement with systems for augmented and virtual reality. Trends in diagnostic and interventional technologies show how their integration is well suited to addressing some of the most intractable problems in pain management. Pain is a profound and unresolved health challenge, and current interventions are not sufficient for safe and effective pain management. Bioelectronics presents solutions for monitoring the symptoms of pain and treating these symptoms in a targeted manner.