Clinical Scorecard: Current Trends and Future Directions in the Use of Artificial Intelligence for Pain Management: A Bibliometric and Visual Review

At a Glance

Key Highlights

• Pain affects over 50 million US adults regularly and is associated with significant physical and psychological burdens.
• AI technologies, particularly machine learning, are increasingly applied to pain management for diagnosis, monitoring, and personalized treatment.
• Bibliometric analysis reveals the evolution of AI in pain management from theoretical exploration to clinical integration over 30 years.

Guideline-Based Recommendations

Diagnosis

• Utilize AI-assisted tools and apps to monitor and predict pain fluctuations for improved assessment.
• Incorporate continuous monitoring technologies in critical care to detect untreated pain risks.

Management

• Implement AI-driven wellness programs to assist patients with musculoskeletal pain.
• Leverage AI for personalized treatment plans based on comprehensive health data analysis.

Monitoring & Follow-up

• Use AI systems for continuous patient condition monitoring, especially in ICU settings.
• Apply machine learning models to track pain score trends over time for proactive management.

Risks

• Recognize the subjective nature of pain and the challenge it poses for accurate AI assessment.
• Ensure AI applications complement, not replace, clinical judgment to avoid mismanagement.

Patient & Prescribing Data

Patients with various pain conditions including neck, shoulder, low back pain, and ICU patients

AI-assisted interventions have shown promise in improving pain outcomes and optimizing resource allocation, though further large-scale studies are needed.

Clinical Best Practices

• Integrate AI technologies as adjuncts to traditional pain assessment and management methods.
• Employ bibliometric and visualization tools to stay informed on evolving research and collaboration opportunities.
• Adopt interdisciplinary approaches combining AI, clinical expertise, and patient-reported data for comprehensive pain care.

References

• IASP Pain Definition
• Anan et al. AI-assisted wellness program study
• Kobayashi et al. ICU pain monitoring
• Rahman et al. Manage My Pain app study
• Web of Science Core Collection