AI-Assisted Rehabilitation for Musculoskeletal Disorders: Comparing 13 Strategies in a Network Meta-Analysis

Physical therapists and physical therapist assistants who work with musculoskeletal caseloads and want a clear, non-technical account of how AI-assisted rehabilitation options compared with one another and with usual care.

Licensed physical therapists and physical therapist assistants in outpatient, post-surgical, home health and telehealth settings. No prior background in artificial intelligence or digital health is assumed.

Intended audience

Physical Therapy

Level

Introductory

Delivery format

Online, asynchronous, self-paced course: interactive reading with audio or video, and a scored post-test.

Based on

This course is based on a single published source, summarized and appraised in plain language:

Luo Z, Wang Y, Zhang T, Wang J. Effectiveness of AI-assisted rehabilitation for musculoskeletal disorders: a network meta-analysis of pain, range of motion, and functional outcomes. Front Bioeng Biotechnol. 2025;13:1660524. doi:10.3389/fbioe.2025.1660524. PMID: 41180836; PMCID: PMC12571919.

Systematic review registration: PROSPERO CRD420251057777.

No claims beyond those supported by this source are presented in this course.

After completing this course, you will be able to:

1. Describe what the network meta-analysis studied, which populations and interventions were included, and how the 13 AI-assisted rehabilitation categories were defined.
2. Name the intervention categories the authors ranked highest for pain relief, for functional outcomes, and for range of motion, and those that ranked lowest.
3. State the limitations the authors identified, including the short follow-up periods and variability across trials, and explain how those limits shaped their conclusions.

1. Start Here: how this course works and what the source is
2. Podcast: AI-Assisted Rehabilitation for Musculoskeletal Disorders (about ten minutes)
3. What the Review Asked and Who Was Included
4. How the Thirteen AI Rehabilitation Categories Were Built and Compared
5. Which Approaches Ranked Highest for Pain, Function and Motion
6. Why the Authors Urged Caution: Short Follow-Up and Mixed Trial Quality
7. From Rankings to the Clinic: Feasibility, Cost and Patient Fit
8. Key Takeaways
9. Post-test and course evaluation

Quinn Millington, PT, DPT, ECS is the instructor of record: the licensed clinician who reviewed this course and signed off on it. The name and credentials print on the certificate.

Disclosures. This course was developed by The ConEd Solution, which has a financial interest in its sale. No outside funding or sponsorship was received. The instructor of record has no affiliation with the authors of the source publication or their institutions.

This course awards 0.5 contact hours (30 min). How you earn them:

1. Complete the course. Every lesson, in order.
2. Pass the post-test. Score 80% or higher. Retakes are allowed.
3. Complete the evaluation. A short form rating each learning objective and the course.
4. Download your certificate. It is issued on completion and stays available in My Account.

The episode time is its measured length. Reading, post-test and evaluation times are estimates.

This course is designed to satisfy the quality requirements state boards set for continuing education. Requirements vary by state, and confirming that a course fits your renewal is up to you. Each state’s own acceptance statement is in the credit panel on this page.

1. This course is based on a single published source, summarized and appraised in plain language:
2. Luo Z, Wang Y, Zhang T, Wang J. Effectiveness of AI-assisted rehabilitation for musculoskeletal disorders: a network meta-analysis of pain, range of motion, and functional outcomes. Front Bioeng Biotechnol. 2025;13:1660524. doi:10.3389/fbioe.2025.1660524. PMID: 41180836; PMCID: PMC12571919.
3. Systematic review registration: PROSPERO CRD420251057777.
4. No claims beyond those supported by this source are presented in this course.