College of Nursing, University of Iowa, Iowa City, Iowa, USA.
Rory Meyers College of Nursing, New York University, New York, New York, USA.
J Am Med Inform Assoc. 2023 Feb 16;30(3):570-587. doi: 10.1093/jamia/ocac231.
Over 20% of US adults report they experience pain on most days or every day. Uncontrolled pain has led to increased healthcare utilization, hospitalization, emergency visits, and financial burden. Recognizing, assessing, understanding, and treating pain using artificial intelligence (AI) approaches may improve patient outcomes and healthcare resource utilization. A comprehensive synthesis of the current use and outcomes of AI-based interventions focused on pain assessment and management will guide the development of future research.
This review aims to investigate the state of the research on AI-based interventions designed to improve pain assessment and management for adult patients. We also ascertain the actual outcomes of Al-based interventions for adult patients.
The electronic databases searched include Web of Science, CINAHL, PsycINFO, Cochrane CENTRAL, Scopus, IEEE Xplore, and ACM Digital Library. The search initially identified 6946 studies. After screening, 30 studies met the inclusion criteria. The Critical Appraisals Skills Programme was used to assess study quality.
This review provides evidence that machine learning, data mining, and natural language processing were used to improve efficient pain recognition and pain assessment, analyze self-reported pain data, predict pain, and help clinicians and patients to manage chronic pain more effectively.
Findings from this review suggest that using AI-based interventions has a positive effect on pain recognition, pain prediction, and pain self-management; however, most reports are only pilot studies. More pilot studies with physiological pain measures are required before these approaches are ready for large clinical trial.
超过 20%的美国成年人报告称他们每天都会经历疼痛。未经控制的疼痛会导致医疗保健利用率增加、住院、急诊就诊和经济负担加重。使用人工智能 (AI) 方法识别、评估、理解和治疗疼痛可能会改善患者的治疗效果和医疗资源的利用。对以疼痛评估和管理为重点的基于人工智能的干预措施的当前使用和结果进行综合分析，将为未来的研究提供指导。
本综述旨在调查旨在改善成人患者疼痛评估和管理的基于人工智能的干预措施的研究现状。我们还确定了基于人工智能的干预措施对成年患者的实际效果。
搜索的电子数据库包括 Web of Science、CINAHL、PsycINFO、Cochrane CENTRAL、Scopus、IEEE Xplore 和 ACM Digital Library。最初的搜索确定了 6946 项研究。经过筛选，有 30 项研究符合纳入标准。使用批判性评估技能计划来评估研究质量。
本综述提供的证据表明，机器学习、数据挖掘和自然语言处理被用于提高疼痛识别和疼痛评估的效率，分析自我报告的疼痛数据，预测疼痛，并帮助临床医生和患者更有效地管理慢性疼痛。
本综述的研究结果表明，基于人工智能的干预措施对疼痛识别、疼痛预测和疼痛自我管理有积极影响，但大多数报告仅为试点研究。在这些方法准备好进行大规模临床试验之前，需要进行更多针对生理疼痛测量的试点研究。
