Clinical SupportObservational2024

AI Support Systems for OMT Technique Selection

Key Finding

No peer-reviewed clinical decision-support systems currently exist that recommend specific osteopathic manipulative treatment (OMT) techniques based on AI analysis; discussions are largely conceptual and emphasize the need to preserve clinician judgment and individualized care. This area remains speculative and requires foundational work on representing osteopathic reasoning in computable form.

6 min read2 sources cited

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Executive Summary

OMT technique selection involves integrating structural findings, patient preferences, comorbidities, safety considerations, and treatment goals—dimensions that have not yet been encoded into AI-based decision support. Current AI-CDS frameworks focus on pharmacologic and procedural guidelines, with no published systems that map osteopathic diagnostic inputs to recommended OMT techniques.

Osteopathic policy papers acknowledge both the potential and the risk of AI in this space, cautioning that technique selection is highly individualized and that algorithmic recommendations could oversimplify osteopathic practice if not carefully designed and validated. Any future systems would need to be transparent, easily overridden, and grounded in evidence-based OMT research.

Detailed Research

Methodology

This summary is based on reviews of AI-enabled CDS and osteopathic AI policy documents, which reveal a lack of published AI tools for OMT technique selection.

No clinical trials or observational studies were identified that evaluate AI-generated OMT recommendations.

Key Studies

AI-Enabled CDS Frameworks

Design: JAMIA consensus recommendations
Sample: CDS best practices
Findings: Discusses pharmacologic and diagnostic support but does not mention OMT or manual techniques, illustrating the current gap in osteopathic-relevant CDS.
Clinical Relevance: Highlights absence of OMT in AI-CDS literature

AOA Artificial Intelligence in Healthcare Policy (2024)

Design: Professional policy statement
Sample: Guidance for osteopathic physicians
Findings: Emphasizes that AI should augment, not replace, osteopathic clinical reasoning, particularly in OMT where tactile feedback and individualized response are central.
Clinical Relevance: Cautions against algorithmic oversimplification

General OMT Evidence Base

Design: Clinical research literature
Sample: OMT efficacy studies
Findings: Provides the clinical foundation any future AI system would need to encode, but this mapping has not yet been attempted.
Clinical Relevance: Foundation exists but AI translation lacking

Clinical Implications

In current practice, OMT technique selection remains a clinician-led process guided by training, experience, patient response, and available evidence.

AI could eventually support OMT decision-making by summarizing relevant evidence, contraindications, or documentation templates, but not by dictating specific techniques.

Limitations & Research Gaps

The complete absence of AI-driven OMT selection tools in the literature is itself the key limitation.

Research priorities include representing OMT indications, contraindications, and outcomes in structured form, and exploring how AI might support—not replace—technique selection in complex cases.

Osteopathic Perspective

Osteopathic principles emphasize individualized, hands-on care and the physician's tactile and observational skills; reducing OMT technique selection to an algorithm risks undermining this core identity.

Any future AI tools should be designed under DO leadership to ensure they enhance education, safety, and documentation while preserving the central role of clinician judgment and the unity of body, mind, and spirit in treatment decisions.

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
McCoy AB, Wright A, et al. “Recommendations for AI-Enabled Clinical Decision Support.” Journal of the American Medical Informatics Association, 2024;31:2730-2742. DOI: 10.1093/jamia/ocae210

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