Artificial Intelligence-Driven Clinical Decision Support Systems for Improving Diagnostic Accuracy and Personalized Treatment Planning in Physical Therapy - Dhirenbhai Kalal (The Eastasouth Journal of Information System and Computer Science, 2025)
As physical therapy practice moves toward the increasingly large and complex multimodal patient data stream (imaging, gait kinetics, wearable-sensor streams, and patient-reported outcomes), relying on unaided clinical judgment is insufficient for pattern recognition. Twenty-five papers were retrieved between 2018 and 2025 for artificial intelligence (AI) clinical decision support systems (CDSS) related to diagnostic accuracy and personalized treatment planning in physical therapy.Twenty-five papers were identified between 2018 and 2025 for AI clinical decision support systems (CDSS) for diagnostic accuracy and personalized treatment planning in physical therapy. The deep-learning models for knee osteoarthritis grading, low back pain classification, and sarcopenia-related gait screening are analyzed, as well as the large language model-based clinical reasoning models, multi-sensor rehabilitation-monitoring platforms, and myoelectric control systems for upper-limb recovery. The reported diagnostic accuracy of imaging-based models ranges from 86.2% to 92.5% and the evidence from the network meta-analysis suggests that the improvement of pain and ROM outcomes by AI-assisted rehabilitation is greater than conventional rehabilitation. The main barriers to the adoption are clinician trust, burden of integration to the workflow, and data-privacy concerns; while the facilitators are explainability and demonstrated diagnostic benefit. Ethical, legal, and regulatory issues related to the use of AI-CDSS in rehabilitation are also explored in the synthesis. The results confirm the hybrid model (clinician in the loop) of integrating AI-CDSS within the task of physical therapist judgment.