Artificial Intelligence Clinical Decision

Clinical Decision Support, Myofacial Pain Syndrome

Clinical decision, artificial intelligence, pain, myofascial pain syndrome

The goal of this study is to investigate the effect of AI integration into clinical physical therapy clinical decision in improving cost effectiveness and clinical outcomes purposes of the study are: 1. Compare the effectiveness of AI driven and human driven clinical decision in physical therapy clinical practice on management of pain in myofascial pain syndrome. 2. Compare the effectiveness of AI driven and human driven clinical decision in physical therapy clinical practice on improving joint range of motion limitations in myofascial pain syndrome. 3. Compare the effectiveness of AI driven and human driven clinical decision in physical therapy clinical practice on improving muscle strength in myofascial pain syndrome. 4. Compare the effectiveness of AI driven and human driven clinical decision in physical therapy clinical practice on management of functional limitation in myofascial pain syndrome. 5. Compare the effectiveness of AI driven and human driven clinical decision in physical therapy clinical practice on cost-effectiveness in physical therapy management of myofascial pain syndrome.

Evaluation of AI impact on health care processes still needs more investigation whatever the review of literature reveals, there is a great interest in developing AI tools to support clinical workflows, with increasing high-quality evidence being generated. Investigation of the impact of AI integration into the process of physical therapy clinical decision-making in management of myofascial pain syndrome still not examined or tested. The study planned to compare the efficacy using AI model to support physical therapy decision based on clinical problems solving by evaluating clinical outcomes such as pain intensity, muscular strength, joint rang motion, functional level and therapeutic cost effective compared to traditional clinical decision in patient with myofascial pain syndrome. Formulation A well-established detailed input (prompt) created to guide ChatGPT to what is requested and the proper way to process through the input to develop comprehensive, multimodal approaches (different modalities categories as therapeutic exercises, electrotherapy, complementary and manual approaches when indicated, evidence-based physiotherapy treatment protocol for and planning therapeutic interventions based on studies from PEDro, PubMed, Cochrane, Scopus, and Google Scholar. Patients will be assigned to two groups; one receives AI based clinical decision therapeutic interventions compared to the other group which will receive a traditional intervention according to published methods. Outcomes measures will be assed to compare which is more effective and result in best outcomes and less cost. Trail expected to test if it's valuable to use AI module in data analysis to support choice effective intervention and test the concept based on patient clinical problem-solving approach in physical practice against previously tested traditional methods, additional benefits may provide an assessment of a trail to formulate successful prompt to be used in other conditions. Although there are many studies in supporting clinical decision, still there are many questions concerned with the validity , methods , efficacy , cost effectiveness , scope and ethics of application

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