Machine Learning in Pain Medicine: An Up-To-Date Systematic Review

Area of Science:

• Pain Medicine
• Artificial Intelligence
• Data Science

Background:

• Pain significantly impacts global quality of life, necessitating innovative relief strategies.
• Understanding pain's complexity drives research into novel therapeutic approaches.
• This review examines machine learning applications in pain diagnosis, classification, and management.

Purpose of the Study:

• To explore the clinical applications of machine learning in pain medicine.
• To review the current literature on machine learning for pain diagnosis, classification, and management.
• To assess the effectiveness of machine learning in pain management.

Main Methods:

• A systematic literature review was performed.
• The PubMed database was utilized for literature retrieval.
• Included studies focused on pain and machine learning research.

Main Results:

• Twenty-six papers on pain and machine learning were analyzed.
• Machine learning was most frequently used for pain classification.
• Machine learning also showed utility in pain prediction, management, and diagnosis.

Conclusions:

• Machine learning (ML) is increasingly adopted in pain medicine.
• ML demonstrates superior effectiveness over traditional statistical methods in pain assessment and management.
• The study highlights ML's potential to advance pain relief and patient care.