Framework for Personalized Chronic Pain Management: Harnessing AI and Personality Insights for Effective Care

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Kumar, Akshi; Seewal, Rahul; Jain, Dipika and Kaur, Ravleen. 2024. Framework for Personalized Chronic Pain Management: Harnessing AI and Personality Insights for Effective Care. Journal of Artificial Intelligence and Technology, 4(2), pp. 132-144. ISSN 2766-8649 [Article]

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Official URL: https://ojs.istp-press.com/jait/article/view/457

Abstract or Description

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 intervention

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.37965/jait.2024.0457