人工智能用于疼痛评估的研究进展

The research and development of artificial intelligence technology for automatic pain assessment

关键词:  人工智能  疼痛评估  卷积神经网络  循环神经网络  自然语言处理

Key words:  artificial intelligence  pain assessment  convolutional neural network  recurrent neural network  natural language processing

摘要:

在临床中准确的疼痛评估对于制定有效的治疗计划至关重要。然而，传统的疼痛评估方法依赖于患者的自我报告或者疼痛评估量表，存在一定的局限性。近年来，人工智能(artificial intelligence, AI)技术的发展为疼痛评估(pain assessment, PA)提供了新的视角。本文综述了AI在PA领域的应用现状和未来发展趋势，探讨了AI的工作原理及其在疼痛评估中的应用。本文首先介绍了疼痛评估的重要性和现有方法的不足，然后详细讨论了基于AI的疼痛评估方法，包括基于行为的评估方法和基于神经生理学的评估方法。进一步分析了早期使用的机器学习算法，如支持向量机、决策树和随机森林，以及近期兴起的深度学习技术，包括卷积神经网络(convolutional neural network, CNN)和循环神经网络(recurrent neural networks, RNN)。这些技术的发展极大地提高了疼痛评估的准确性和可靠性。最后，本文讨论了在AI应用于疼痛研究和管理时，可解释性和伦理问题的重要性。本文可为临床提供一个关于AI在疼痛评估中应用的全面视角，并探讨潜在的研究方向，以推动该领域的进一步发展。

Abstract:

Accurate and reliable pain assessment(PA) is crucial for formulating effective treatment plans. However, traditional methods of PA relied on patient self-reporting or pain rating scales with certain limitations. In recent years, the development of artificial intelligence(AI) technology has provided new perspectives for PA. This paper reviews the current state and future trends of AI applications in the field of PA, exploring the working principles of AI and its application in PA. This article first introduces the importance of PA and the shortcomings of existing methods, then discusses in-detail AI-based PA methods, including behavior-based methods and neurophysiology-based pain detection methods. The early machine learning algorithms such as support vector machines, decision trees, and random forests, as well as recently emerging deep learning techniques, including convolutional neural network(CNN) and recurrent neural network(RNN) were further analyzed. The advancement of these technologies has significantly improved the accuracy and reliability of PA. Finally, this article discusses the importance of explainability and ethical issues when AI is applied to pain research and management. The purpose of this paper is to provide readers with a comprehensive perspective on the application of AI in automatic PA and to explore potential research directions to advance the field further.