Clinical Report: Trends and Future Directions of AI in Pain Management

Overview

This bibliometric review analyzes 30 years of global literature on artificial intelligence (AI) applications in pain management, highlighting its evolution from theoretical concepts to clinical integration. It identifies key research hotspots, interdisciplinary collaborations, and emerging trends that promise to enhance personalized pain care.

Background

Pain, defined by the International Association for the Study of Pain as an unpleasant sensory and emotional experience, remains a complex and multidimensional public health challenge affecting over 20% of US adults. Traditional pain management approaches often fail to address the subjective and multifactorial nature of pain, leading to inadequate treatment and increased healthcare utilization. Artificial intelligence, encompassing machine learning and decision support systems, offers promising avenues to improve pain assessment, prediction, and personalized treatment strategies. Bibliometric analysis enables a comprehensive evaluation of research trends and collaborations in this rapidly evolving field.

Data Highlights

The study analyzed 30 years (1995–2024) of literature from the Web of Science Core Collection, including 46,170 co-cited references. It utilized bibliometric coupling and visualization tools such as CiteSpace and VOSviewer to map research evolution, identify influential authors, institutions, and journals, and quantify collaboration patterns across disciplines.

Key Findings

• AI research in pain management has transitioned from theoretical exploration to clinical application over the past three decades.
• Interdisciplinary collaboration is increasing, integrating fields such as machine learning, clinical medicine, and behavioral science.
• Key research hotspots include AI-assisted pain assessment, prediction of pain fluctuations, and continuous monitoring in critical care settings.
• Bibliometric coupling revealed influential authors and institutions driving innovation and knowledge dissemination in this domain.
• Visualization tools facilitated understanding of evolving research themes and strategic alliances for future investigations.

Clinical Implications

The integration of AI technologies in pain management can enhance personalized diagnosis, optimize treatment plans, and improve patient outcomes by addressing the subjective nature of pain. Clinicians should consider incorporating AI-driven tools for continuous pain monitoring and predictive analytics to better tailor interventions and allocate healthcare resources efficiently.

Conclusion

This bibliometric and visual review underscores the growing impact of AI in pain management, highlighting its potential to transform clinical practice through interdisciplinary research and technological innovation. Continued collaboration and focused research are essential to fully realize AI's benefits in improving pain care.

References

1. International Association for the Study of Pain (IASP) -- Definition of Pain
2. Anan et al. 202X -- AI-assisted wellness program for neck and shoulder pain
3. Kobayashi et al. 202X -- Continuous ICU pain monitoring and survival
4. Rahman et al. 202X -- Predicting pain fluctuations using Manage My Pain app
5. Web of Science Core Collection -- Data source for bibliometric analysis