Development and Preliminary Evaluation of an AI-Assisted Clinical Decision Support System for Personalized Musculo-Skeletal Pain Management in Integrative Nursing Practice

Background/Objectives: Musculoskeletal pain is a common condition that negatively affects quality of life and functional capacity. Integrative nursing emphasizes patient-centered and non-pharmacological approaches; however, variability in clinical decision-making persists due to reliance on clinician experience. This study aimed to design, retrospectively train, and evaluate the preliminary feasibility of a machine learning-based clinical decision support framework for personalized pain management within an integrative nursing approach in a Traditional and Complementary Medicine setting. Methods: This study included a retrospective cohort of 487 patients (mean age: 48.2 ± 14.7 years; 63.0% women) for machine learning model development and evaluation. The classification model predicted binary clinical improvement, defined as a ≥3-point reduction in VAS pain score, whereas the regression model predicted post-treatment VAS score. The resulting models were incorporated into the GETAIA prototype to generate modality-specific predicted post-treatment VAS scores and probabilities of clinically meaningful improvement for the three available T&CM modalities; these predicted profiles were compared to provide a model-derived treatment recommendation for clinical review. In addition, a prospective, non-randomized pilot involving 42 patients (21 AI-assisted and 21 standard care) was conducted to assess preliminary point-of-care workflow feasibility. Results: Significant reductions in pain intensity were observed across all treatment groups (p < 0.001). Clinically meaningful improvement rates were 76.2% for cupping therapy, 68.3% for acupuncture, and 58.8% for mesotherapy (p = 0.007). The prototype demonstrated 78.3% concordance with historical clinician treatment decisions, which was interpreted as an internal model-fidelity measure reflecting the reproduction of historical clinical decision patterns rather than evidence of improved decision-making, clinical effectiveness, or external validity. During a preliminary prospective feasibility implementation, clinically favorable numerical trends were observed in the AI-assisted group compared with standard care, including clinically meaningful improvement in 85.7% (18/21) versus 71.4% (15/21) of patients (p = 0.449) and mean VAS reductions of 3.9 ± 1.2 versus 3.2 ± 1.4 (p = 0.092). These exploratory differences were not statistically significant and were not intended to establish comparative clinical effectiveness. On the held-out test set, the primary XGBoost classification model achieved an accuracy of 82.6% and an AUC-ROC of 0.87 (95% CI: 0.82–0.92), while the regression model predicting post-treatment VAS achieved an MAE of 0.87. Key predictors of improvement included diagnosis, baseline pain intensity, and age. Conclusions: The findings support the preliminary development and evaluation of a machine learning-based decision-support framework and GETAIA prototype for individualized pain management within an integrative nursing context. The retrospective analysis demonstrated predictive performance of the evaluated models, while the small, non-randomized prospective pilot provided preliminary information on point-of-care workflow integration. These findings do not establish the clinical effectiveness, superiority, routine-care readiness, or external validity of a fully implemented clinical decision support system. Larger, prospective, multicenter studies are required to evaluate clinical effectiveness, generalizability, usability, and implementation readiness.