Advanced AI Training for Physical Therapy & Rehabilitation Practitioners & Students

via OpenLearning

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Provider

OpenLearning
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Sessions

Self-Paced
Level

Advanced

Found in

Overview

Master AI & Machine Learning for 50% Off

Go under the hood of AI — neural networks, real-world applications & more. Designed by UNSW experts.

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Gain advanced, clinically-focused skills in applying artificial intelligence tools within physical therapy and rehabilitation practice through this comprehensive course designed for both practitioners and students. Explore structured verification frameworks — including MOVE, LOAD-R, RISK-R, and SCOPE-A — to critically evaluate AI-generated outputs across movement analysis, exercise load management, recovery prediction, and remote monitoring before implementing them in clinical settings. Develop the ability to interpret markerless motion capture, pose estimation, and gait analytics outputs while recognising failure modes and distinguishing AI-detected patterns from clinically meaningful dysfunction. Apply the LOAD-R framework to assess AI-generated exercise dose and progression recommendations, and interpret recovery trajectory forecasts as probability-based predictions rather than deterministic clinical conclusions. Evaluate AI-driven wearable data, smart insoles, and pressure mapping analyses using anatomical and biomechanical knowledge to ensure safe patient care. Engage with the ethical and professional dimensions of AI-assisted rehabilitation, including how to support biopsychosocial assessment in chronic pain without reducing complex patient experiences to algorithmic labels, and how to communicate AI-generated risk scores responsibly. Use the SCOPE-A framework to identify when AI recommendations approach or exceed professional scope of practice, and understand allied health-specific informed consent and documentation requirements. Examine discipline-specific AI applications across physiotherapy, occupational therapy, chiropractic, podiatry, osteopathy, and exercise physiology, identifying unique failure modes, scope sensitivities, and clinical validation requirements for each field. Practise integrating all four frameworks across six simulated patient encounters to build safe, defensible AI-assisted decision-making skills in a multidisciplinary environment. Conclude by designing a personal AI learning plan tailored to rehabilitation practice, articulating AI literacy competencies relevant to professional development and job interviews, and understanding the medico-legal position of students operating in AI-assisted clinical environments.

Syllabus

Evaluate AI-generated movement analysis outputs (e.g., markerless motion capture, pose estimation, gait analytics) using the MOVE verification framework.
Verify AI-generated exercise load and progression recommendations using the LOAD-R framework before clinical implementation.
Interpret AI recovery predictions and return-to-function forecasts, distinguishing probabilistic outputs from deterministic clinical conclusions.
Assess AI-generated biomechanical and gait analyses from wearables, smart insoles, and pressure mapping systems using anatomical and biomechanical knowledge.
Use AI tools to support biopsychosocial assessment in chronic pain without reducing complex patient experiences to algorithmic labels.
Evaluate AI-driven remote monitoring systems (e.g., telerehabilitation analysis, fall detection, adherence tracking) using the RISK-R safety protocol.
Identify when AI outputs approach or exceed professional scope of practice using the SCOPE-A framework.
Apply AI tools across movement analysis, load management, chronic pain monitoring, ethics, and specialist rehabilitation practice using structured clinical verification frameworks while maintaining patient safety and professional scope.
Evaluate AI-generated movement analysis outputs using the MOVE verification framework, distinguishing between AI-detected movement patterns and clinically meaningful dysfunction while recognising failure modes in markerless motion capture and gait analysis tools.
Apply the LOAD-R framework to verify AI-generated exercise dose and load recommendations, interpret recovery trajectory forecasts as probability-based predictions, and identify high-risk populations where AI load models carry the greatest risk of misapplication.
Analyse AI-generated biopsychosocial risk scores for chronic pain without communicating diagnostic labels to patients and apply the RISK-R safety protocol to evaluate remote monitoring AI alerts before clinical escalation.
Implement the SCOPE-A framework to protect professional registration boundaries when AI tools generate recommendations beyond scope, and apply allied health-specific informed consent and documentation requirements for AI-assisted practice.
Evaluate AI applications specific to physiotherapy, occupational therapy, chiropractic, podiatry, osteopathy, and exercise physiology, identifying discipline-specific failure modes, scope sensitivities, and clinical validation requirements.
Integrate the MOVE, LOAD-R, RISK-R, and SCOPE-A frameworks across six simulated patient encounters to demonstrate safe, defensible AI-assisted rehabilitation decision-making in a multidisciplinary clinical environment.
Design a personal AI learning plan for rehabilitation practice by applying foundational frameworks in supervised clinical contexts, articulating AI literacy competencies for job interviews, and understanding the student medico-legal position in AI-assisted environments.