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    AI In Rehabilitation: Evidence-Based Update

    Type: On Demand   |    Credit: 2.0   |    Price: $37.97
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    Course Description

    Unlock the future of clinical practice with AI in Rehabilitation: An Evidence-Based Update, a premier continuing education course designed specifically for physical therapists, occupational therapists, and nurses. As artificial intelligence fundamentally reshapes rehabilitation medicine, this course provides you with the essential competencies to navigate transformative tools in patient assessment, clinical decision-making, and outcome prediction.

    You will explore high-impact data from systematic reviews and randomized controlled trials, learning how AI-driven interventions- from robotic exoskeletons to ambient AI scribes- can significantly enhance functional outcomes and reduce documentation burnout. By bridging the gap between technological potential and real-world clinical readiness, this course empowers you to deliver a modern standard of care that meets the evolving expectations of patients and payers alike. Gain the competitive edge and ethical framework necessary to lead in an increasingly data-driven healthcare landscape.

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    AI In Rehabilitation: Evidence-Based Update

    $37.97

    Additional Course Details

    Course Outline

    0:00 - Introduction

    5:28 - Module 1: AI Assessment Technologies

    17:37 - Module 2: Effectiveness of AI Rehab Interventions

    30:34 - Module 3: Patient-Specific Effectiveness

    41:43 - Module 4: Discipline-Specific AI Tech

    54:47 - Module 5: Clinician Training & Adoption

    1:06:16 - Module 6: AI-Powered Clinical Documentation

    1:17:06 - Module 7: Ethics and Implementation

    1:28:47 - Module 8: Practical AI Literacy

    1:59:52 - Conclusion

    2:14:00 - End


    Learning Objectives

    1. Identify validated AI assessment technologies and their appropriate rehabilitation settings.
    2. Compare the effectiveness of AI-assisted rehabilitation interventions versus conventional therapy based on current evidence.
    3. Interpret accuracy metrics and key predictors used in AI models for rehabilitation outcomes.
    4. Distinguish AI adoption patterns and implementation barriers across rehabilitation disciplines.
    5. Apply the six-domain AI competency framework to rehabilitation professional development.
    6. Evaluate the accuracy and limitations of AI-powered clinical documentation systems in rehabilitation.
    7. Select ethically sound strategies for equitable AI implementation in rehabilitation practice.

    Course Instructor

    Author: Anne Osborn, PT, MPT
    Personnel Disclosure:

    Financial – ​​​​​​​​​Anne Osborn, PT, MPT is the member manager of Ridley Learning. She receives compensation for the authorship of this course.

    Nonfinancial - no relevant nonfinancial relationship exists.

    No relevant conflicts of interest exist for any member of the activity planning committee.

    Course Media / Format

    • Video

    Instructional Level

    Intermediate

    Target Audience

    • Occupational Therapist
    • Occupational Therapist Assistant
    • Physical Therapist
    • Physical Therapist Assistant
    • Registered Nurse

    Course Participation & Disclosure

    This course does not focus solely on any specific product or service. This course was prompted and edited with the help of artificial intelligence.
    To receive credit for completion, the post-course exam must be passed with a score of 70% or above. Scores less than 70% must be re-taken until a passing score of 70% is achieved to receive a certificate of successful completion. You will have unlimited exam attempts.
    You will have 1 year from the date of enrollment to complete your course.

    Cancellation Policy: For activity cancellation, returns, or complaint resolution, please contact us by email help@ridleylearning.com. We have a 100% satisfaction guarantee. Refunds will be issued for courses who's credit has not been issued (exams have not been completed), or for any course that has been rejected by your board of approval. Webinar cancellations and exchanges must be completed 24 hours prior to the scheduled start time.