A roadmap for artificial intelligence in pain medicine: current status, opportunities, and requirements.
Authors
Affiliations (6)
Affiliations (6)
- Departments of Anesthesiology, Translational Neuroscience, and Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.
- Pain Outcomes Lab, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.
- Department of Anaesthesia, University College London Hospitals NHS Foundation Trust, London, UK.
- Department of Targeted Intervention, University College London, London, UK.
- Department of Anesthesiology and Perioperative Care, University of California Irvine, Irvine, California, USA.
- Division of Pain Medicine, University Hospitals Cleveland, Cleveland, Ohio, USA.
Abstract
Artificial intelligence (AI) represents a transformative opportunity for pain medicine, offering potential solutions to longstanding challenges in pain assessment and management. This review synthesizes the current state of AI applications with a strategic framework for implementation, highlighting established adaptation pathways from adjacent medical fields. In acute pain, AI systems have achieved regulatory approval for ultrasound guidance in regional anesthesia and shown promise in automated pain scoring through facial expression analysis. For chronic pain management, machine learning algorithms have improved diagnostic accuracy for musculoskeletal conditions and enhanced treatment selection through predictive modeling. Successful integration requires interdisciplinary collaboration and physician coleadership throughout the development process, with specific adaptations needed for pain-specific challenges. This roadmap outlines a comprehensive methodological framework for AI in pain medicine, emphasizing four key phases: problem definition, algorithm development, validation, and implementation. Critical areas for future development include perioperative pain trajectory prediction, real-time procedural guidance, and personalized treatment optimization. Success ultimately depends on maintaining strong partnerships between clinicians, developers, and researchers while addressing ethical, regulatory, and educational considerations.