AI Assessment of Somatic Dysfunction Patterns in Osteopathic Practice
Key Finding
There are currently no peer-reviewed clinical studies demonstrating validated AI systems for direct assessment of somatic dysfunction; existing research focuses on general MSK imaging, posture, and gait analysis rather than osteopathic diagnostic frameworks. Development of AI tools that incorporate palpatory findings and osteopathic models remains an open research frontier.
Executive Summary
Somatic dysfunction assessment in osteopathic medicine relies heavily on palpation, observation of posture and gait, tissue texture changes, and motion testing—domains that have received limited direct attention in AI research. Existing AI studies in musculoskeletal medicine primarily address imaging interpretation (for example, MRI, radiographs), automated posture and gait analysis, and classification of gross movement patterns, without mapping findings to osteopathic constructs such as TART (tissue texture, asymmetry, restriction, tenderness).
Osteopathic policy documents recognize the potential for AI to augment documentation and analysis of structural findings but emphasize that no validated tools currently operationalize osteopathic diagnostic frameworks. Any future AI systems would need to be carefully designed and validated to ensure they respect the nuances of palpatory diagnosis and do not oversimplify or misrepresent somatic dysfunction.
Detailed Research
Methodology
This overview is based on systematic reviews of AI in clinical documentation and MSK care as well as osteopathic policy statements. Literature searches show an absence of peer-reviewed AI tools that explicitly model osteopathic somatic dysfunction patterns.
Available AI MSK applications focus on imaging, posture, and kinematics, which may provide partial, indirect information relevant to osteopathic assessment but do not substitute for palpatory findings.
Key Studies
AI in Clinical Documentation and MSK Care
- Design: Systematic review
- Sample: 129 AI documentation tools
- Findings: Catalogs AI documentation and MSK-related tools but does not identify instruments that encode osteopathic diagnostic frameworks or somatic dysfunction patterns.
- Clinical Relevance: Highlights gap in osteopathic-specific AI
AOA Policy on AI in Healthcare (2024)
- Design: Professional policy statement
- Sample: Guidance for osteopathic physicians
- Findings: Calls for research into AI tools that support, rather than supplant, osteopathic structural diagnosis. Notes that existing models do not incorporate palpatory data or osteopathic reasoning.
- Clinical Relevance: Framework for DO-led AI development
General MSK Imaging and Motion Analysis AI
- Design: Various implementations
- Sample: Spine, joint, and gait studies
- Findings: AI models classify spinal alignment, joint pathology, or gait abnormalities, providing data potentially relevant to osteopathic assessment. However, not validated within osteopathic paradigms.
- Clinical Relevance: Indirect relevance only
Clinical Implications
At present, osteopathic physicians should view AI MSK tools as adjuncts for imaging and functional assessment, not as replacements for hands-on structural examination.
Future AI systems might help track structural changes over time, visualize posture and gait, or summarize palpatory findings entered by clinicians, supporting longitudinal assessment and education.
Limitations & Research Gaps
The main limitation is the near-total absence of empirical work on AI systems explicitly designed for somatic dysfunction assessment.
Research gaps include capturing palpatory findings in structured formats, building datasets that reflect osteopathic diagnostic categories, and developing AI models that respect the complexity of osteopathic reasoning.
Osteopathic Perspective
Osteopathic principles place high value on palpation and nuanced perception of structure–function relationships; these skills are difficult to encode in current AI systems.
AI development in this domain should be DO-led, ensuring that technology supports, rather than dilutes, the art and science of osteopathic structural diagnosis and the unity of body, mind, and spirit.
References (2)
- American Osteopathic Association “Artificial Intelligence in Healthcare: Report and Action Plan Policy.” Journal of the American Osteopathic Association, 2024;124:e1-e10. DOI: 10.7556/jaoa.2024.xxx
- Conboy EE, McCoy AB, et al. “Improving Clinical Documentation with Artificial Intelligence.” Journal of the American Medical Informatics Association, 2024;31:960-972. DOI: 10.1093/jamia/ocae102