个性化慢性疼痛管理框架:利用人工智能和个性洞察力实现有效护理

Akshi Kumar, Rahul Seewal, Dipika Jain, Ravleen Kaur
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摘要

本文介绍了一种利用人工智能(AI)和人格洞察力进行个性化慢性疼痛管理的前沿框架。它探讨了人格特质与疼痛感知、表达和管理之间错综复杂的关系,确定了影响个人疼痛体验的关键相关因素。通过将人格心理学与人工智能驱动的人格评估相结合,该框架为针对每位患者的独特人格特征定制慢性疼痛管理策略提供了一种新方法。它强调了五大人格理论和迈尔斯-布里格斯类型指标(MBTI)等成熟人格理论在制定个性化疼痛管理计划中的相关性。此外,论文还介绍了多模态人工智能驱动的人格评估,强调了实施该评估所需的伦理考虑因素和数据收集过程。通过案例研究,本文举例说明了这一框架如何能更有效地缓解疼痛,并以病人为中心,最终提高整体健康水平。最后,本文认为有必要开展 "人工智能驱动的整体疼痛管理计划",该计划有可能通过提供个性化、数据驱动的解决方案来改变慢性疼痛管理,并产生多方面的研究影响,从而影响临床实践、患者预后、医疗保健政策以及更广泛的科学界对个性化医学和人工智能驱动的干预措施的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Framework for Personalized Chronic Pain Management: Harnessing AI and Personality Insights for Effective Care
This paper introduces a cutting-edge framework for personalized chronic pain management, leveraging the power of artificial intelligence (AI) and personality insights. It explores the intricate relationship between personality traits and pain perception, expression, and management, identifying key correlations that influence an individual's experience of pain. By integrating personality psychology with AI-driven personality assessment, this framework offers a novel approach to tailoring chronic pain management strategies for each patient's unique personality profile. It highlights the relevance of well-established personality theories such as the Big Five and the Myers-Briggs Type Indicator (MBTI) in shaping personalized pain management plans. Additionally, the paper introduces multimodal AI-driven personality assessment, emphasizing the ethical considerations and data collection processes necessary for its implementation. Through illustrative case studies, the paper exemplifies how this framework can lead to more effective and patient-centered pain relief, ultimately enhancing overall well-being. In conclusion, the paper positions the need of an "AI-Powered Holistic Pain Management Initiative" which has the potential to transform chronic pain management by providing personalized, data-driven solutions and create a multifaceted research impact influencing clinical practice, patient outcomes, healthcare policy, and the broader scientific community's understanding of personalized medicine and AI-driven interventions.
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