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Daring discourse: artificial intelligence in pain medicine, opportunities and challenges
  1. Meredith C B Adams1,
  2. Ariana M Nelson2 and
  3. Samer Narouze3
  1. 1Departments of Anesthesiology, Biomedical Informatics, Physiology & Pharmacology, and Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
  2. 2Department of Anesthesiology and Perioperative Care, University of California Irvine, Irvine, California, USA
  3. 3Western Reserve Hospital, Cuyahoga Falls, Ohio, USA
  1. Correspondence to Dr Ariana M Nelson, Department of Anesthesiology and Perioperative Care, University of California Irvine, Irvine, CA 92868, USA; arianamn{at}


Artificial intelligence (AI) tools are currently expanding their influence within healthcare. For pain clinics, unfettered introduction of AI may cause concern in both patients and healthcare teams. Much of the concern stems from the lack of community standards and understanding of how the tools and algorithms function. Data literacy and understanding can be challenging even for experienced healthcare providers as these topics are not incorporated into standard clinical education pathways. Another reasonable concern involves the potential for encoding bias in healthcare screening and treatment using faulty algorithms. And yet, the massive volume of data generated by healthcare encounters is increasingly challenging for healthcare teams to navigate and will require an intervention to make the medical record manageable in the future. AI approaches that lighten the workload and support clinical decision-making may provide a solution to the ever-increasing menial tasks involved in clinical care. The potential for pain providers to have higher-quality connections with their patients and manage multiple complex data sources might balance the understandable concerns around data quality and decision-making that accompany introduction of AI. As a specialty, pain medicine will need to establish thoughtful and intentionally integrated AI tools to help clinicians navigate the changing landscape of patient care.

  • Economics
  • Diagnostic Techniques and Procedures
  • Treatment Outcome

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  • Twitter @meredithadamsmd, @ANels_MD

  • Contributors MCBA, AMN and SN contributed to analysis of the literature, design of the manuscript and to the writing of the manuscript.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests MCBA receives research support from the NIH HEAL Initiative through the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under grant number K08EB022631 and the National Institute of Drug Abuse under grant number R24DA055306, R24DA055306-01S1. AMN receives research support from Veoneer to investigate a biomarker for cannabis intoxication that can be used in roadside testing.

  • Provenance and peer review Not commissioned; externally peer reviewed.