Departments of Anesthesiology, Biomedical Informatics, Physiology & Pharmacology, and Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Department of Anesthesiology and Perioperative Care, University of California Irvine, Irvine, California, USA
Reg Anesth Pain Med. 2023 Sep;48(9):439-442. doi: 10.1136/rapm-2023-104526. Epub 2023 May 11.
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.
人工智能（AI）工具目前正在扩大其在医疗保健领域的影响力。对于疼痛诊所来说，无限制地引入 AI 可能会引起患者和医疗团队的担忧。这些担忧主要源于缺乏社区标准和对工具及算法运作方式的理解。即使对于经验丰富的医疗保健提供者来说，数据素养和理解也可能具有挑战性，因为这些主题并未纳入标准临床教育途径。另一个合理的担忧涉及使用有缺陷的算法在医疗保健筛查和治疗中编码偏差的可能性。然而，医疗保健接触产生的大量数据正使医疗团队越来越难以驾驭，并且需要采取干预措施来使医疗记录在未来变得易于管理。减轻工作负荷并支持临床决策的 AI 方法可能为临床护理中涉及的日益繁重的琐碎任务提供解决方案。疼痛提供者与患者建立更高质量联系并管理多个复杂数据源的潜力可能会平衡引入 AI 所伴随的数据质量和决策方面的可理解的担忧。作为一个专业领域，疼痛医学将需要建立深思熟虑和有意整合的 AI 工具，以帮助临床医生应对患者护理不断变化的格局。
