Exploring AI for pain research management: A deep dive investigative exploration

Chronic pain still remains a complex healthcare challenge impacting millions of people worldwide, demanding innovative solutions to enhance patient outcomes and alleviate the burden towards healthcare systems. This research investigates the transformative potential of Artificial Intelligence (AI) in chronic pain management, emphasizing its application in personalized diagnostics, predictive modeling, and optimized treatment strategies. Leveraging advanced AI technologies such as machine learning and neural networks, this study explores real-time pain assessment, AI-driven pain intensity analysis, and predictive tools for chronic pain management that adapt to individual patient profiles. Additionally, it provides a critical evaluation of the ethical considerations involved, particularly in data privacy, algorithmic fairness, and patient consent, and discusses frameworks like GDPR that guide towards responsible data handling within AI healthcare applications. Practical implementation challenges are also examined, including the infrastructural demands of AI integration and the need for interdisciplinary collaboration among healthcare professionals. With a comprehensive analysis of current research and applications, this study proposes a framework for effectively deploying AI in pain management, aimed at advancing patient outcomes, reducing opioid dependency, and improving care efficiency. This exploration seeks to position AI as a viable tool in future pain research management, facilitating a holistic approach to chronic pain that considers both technical and psychosocial dimensions.

Authors

Institutions

Publication Details

Journal
Journal of Pain Research and Management
Published
2025-03-13
DOI
https://doi.org/10.46439/painresearch.1.004
Citations
4
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
2.62
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring AI for pain research management: A deep dive investigative exploration

Zarif Bin Akhtar
4 citations
Journal of Pain Research and Management
Artificial Intelligence in Healthcare and Education
2.62
article

Exploring AI for pain research management: A deep dive investigative exploration

Zarif Bin Akhtar
article en
4 citations

Abstract

Chronic pain still remains a complex healthcare challenge impacting millions of people worldwide, demanding innovative solutions to enhance patient outcomes and alleviate the burden towards healthcare systems. This research investigates the transformative potential of Artificial Intelligence (AI) in chronic pain management, emphasizing its application in personalized diagnostics, predictive modeling, and optimized treatment strategies. Leveraging advanced AI technologies such as machine learning and neural networks, this study explores real-time pain assessment, AI-driven pain intensity analysis, and predictive tools for chronic pain management that adapt to individual patient profiles. Additionally, it provides a critical evaluation of the ethical considerations involved, particularly in data privacy, algorithmic fairness, and patient consent, and discusses frameworks like GDPR that guide towards responsible data handling within AI healthcare applications. Practical implementation challenges are also examined, including the infrastructural demands of AI integration and the need for interdisciplinary collaboration among healthcare professionals. With a comprehensive analysis of current research and applications, this study proposes a framework for effectively deploying AI in pain management, aimed at advancing patient outcomes, reducing opioid dependency, and improving care efficiency. This exploration seeks to position AI as a viable tool in future pain research management, facilitating a holistic approach to chronic pain that considers both technical and psychosocial dimensions.

Journal of Pain Research and ManagementVol. 1(1)
Institute of Electrical and Electronics Engineers (US)
Openalex Percentile: Top 10%
Artificial Intelligence in Healthcare and Education
2.62
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.