Guidance for stakeholders
Core AI guidance for all physiotherapy stakeholders
Developing AI literacy and skills
AI introduces new technological competencies required across all physiotherapy domains.
All physiotherapy professionals should develop foundational AI literacy to understand AI capabilities, applications, and limitations. This includes the ability to critically evaluate AI outputs, recognise potential biases, and understand how AI-generated recommendations fit within established physiotherapy practice. Continuous professional development in AI applications will be essential as these technologies evolve. Organizations should support structured training opportunities while individuals cultivate curiosity about emerging technologies.
Balancing AI and human expertise
AI offers powerful capabilities but raises questions about maintaining essential human skills.
AI should complement, not replace, human expertise. Maintain strong clinical reasoning, hands-on skills, and independent judgment while leveraging AI as a supportive tool. The therapeutic relationship and human touch remain central to physiotherapy practice. Focus on using AI to enhance efficiency in appropriate areas so more time can be devoted to aspects of care that require human skills. Mentoring relationships should emphasize this balance, with experienced practitioners guiding how to integrate AI tools while preserving core physiotherapy competencies.
Critical evaluation and reflection
AI provides recommendations that require professional judgement and critical assessment.
Adopt a reflective approach to AI integration, critically assessing AI recommendations and outputs before implementation. Foster a culture of thoughtful evaluation that weighs AI suggestions against clinical expertise, research evidence, and patient needs. Encourage regular reflection on how AI is changing practice patterns and ensure that professional judgment remains the final authority in decision-making processes. Document reasoning when accepting or rejecting AI recommendations in clinical, educational, or research contexts.
Ethical use and governance
AI raises new ethical considerations around data use, privacy, and potential biases.
Prioritize ethical considerations in AI adoption, including patient privacy, informed consent, and data security. Be transparent with patients, students, and research participants about AI use. Develop clear policies for appropriate AI implementation that align with professional values and standards. Consider implications for equity and access, ensuring AI doesn't exacerbate existing healthcare disparities. Establish ethical review processes for AI applications, particularly when handling sensitive data or making significant decisions.
Policy development and standards
AI integration requires new professional standards, competency frameworks, and guidelines.
Develop comprehensive policies, guidelines, and standards for responsible AI use in physiotherapy. Professional organizations should create position statements and ethical frameworks to guide implementation. Regulatory bodies should establish adaptive frameworks that address AI-related risks without stifling innovation. Educational programs need clear policies on AI use in assessment, while research requires transparent reporting standards for AI methods. All policies should be regularly updated to reflect technological advancements.
Ensuring equitable access
AI adoption may create disparities between well-resourced and under-resourced settings.
Work to ensure equitable access to AI tools across different settings, preventing digital divides between well-resourced and under-resourced institutions or practices. Consider how AI implementation might affect various patient populations differently, and develop strategies to maintain equitable care. In education, ensure all students have equal opportunities to develop AI competencies regardless of their technological background. Research funding should support diverse settings to prevent AI capabilities becoming concentrated only in elite institutions.
Sustainability of AI use
AI implementation has both environmental impacts and implications for human resources.
Consider both ecological and resource sustainability in AI implementation. Be mindful of the environmental impact of high-compute AI systems, especially when deployed at scale. Develop policies that promote energy-efficient AI use and consider the carbon footprint of different applications. From a human resource perspective, ensure AI integration doesn't lead to staff burnout through implementation fatigue or constant technological change. Create sustainable adoption models that consider workforce wellbeing, allowing adequate time for adaptation and learning. Balance technological advancement with sustainable workloads and realistic implementation timelines.
Domain-specific guidance
Clinical practice
Patient-centred implementation
Prioritise patient needs and preferences when implementing AI. Involve patients in discussions about AI use in their care, ensuring they understand how and why AI tools are being used.
Maintaining therapeutic relationships
Focus on using AI to enhance, rather than replace, the therapeutic relationship. Ensure technology doesn't create barriers between practitioner and patient.
Clinical decision support
Use AI as a decision support tool while maintaining clinical autonomy. Recognize that AI recommendations should inform, but not dictate, clinical decisions.
Administrative efficiency
Leverage AI to reduce administrative burdens, allowing more time for direct patient care and clinical reasoning.
Supervision in clinical settings
Clinical supervisors should guide the appropriate integration of AI tools while ensuring practitioners develop and maintain core skills.
Education
Curriculum integration
Incorporate AI literacy throughout physiotherapy curricula, developing new competency frameworks that address both technological and traditional skills.
Assessment innovation
Develop new assessment methods that acknowledge AI capabilities while ensuring they evaluate true student competency.
Simulation enhancement
Use AI to create more sophisticated and responsive simulation environments while maintaining opportunities for hands-on practice.
Placement considerations
Develop guidelines for appropriate AI use during clinical placements, balancing technological assistance with authentic learning experiences.
Faculty development
Support teaching faculty in developing both the technical and pedagogical skills needed to effectively integrate AI into their teaching.
Academic integrity
Establish clear expectations and policies regarding appropriate AI use in academic work, distinguishing between helpful learning applications and problematic shortcuts.
Research
Transparent methodology
Document AI methods transparently in research publications, including details about models used, training data, and limitations.
Data integrity
Maintain rigorous standards for data collection, validation, and analysis when using AI tools in research.
Hypothesis generation
Use AI as a complement to human creativity in generating research questions, not as a replacement for researcher insight.
Literature synthesis
Leverage AI for efficient literature review and synthesis while maintaining critical evaluation of sources and findings.
Collaborative research
Utilize AI to facilitate collaboration across distances and disciplines, expanding research networks and perspectives.
Verification protocols
Implement processes to verify AI-generated analyses and results, ensuring research integrity and reproducibility.
Research supervision
Guide research students in balancing AI assistance with developing independent scientific thinking and methodological skills.
Open science
Promote open science practices that make AI models, methodologies, and data available for scrutiny and replication when possible.
Developing AI literacy and skills
All physiotherapy professionals should develop foundational AI literacy to understand AI capabilities, applications, and limitations. This includes the ability to critically evaluate AI outputs, recognize potential biases, and understand how AI-generated recommendations fit within established physiotherapy practice. Continuous professional development in AI applications will be essential as these technologies evolve. Organizations should support structured training opportunities while individuals cultivate curiosity about emerging technologies.
Balancing AI and human expertise
AI should complement, not replace, human expertise. Maintain strong clinical reasoning, hands-on skills, and independent judgment while leveraging AI as a supportive tool. The therapeutic relationship and human touch remain central to physiotherapy practice. Focus on using AI to enhance efficiency in appropriate areas, so more time can be devoted to aspects of care that require human skills. Mentoring relationships should emphasize this balance, with experienced practitioners guiding how to integrate AI tools while preserving core physiotherapy competencies.
Critical evaluation and reflection
Adopt a reflective approach to AI integration, critically assessing AI recommendations and outputs before implementation. Foster a culture of thoughtful evaluation that weighs AI suggestions against clinical expertise, research evidence, and patient needs. Encourage regular reflection on how AI is changing practice patterns, and ensure that professional judgment remains the final authority in decision-making processes. Document reasoning when accepting or rejecting AI recommendations in clinical, educational, or research contexts.
Ethical use and governance
Prioritise ethical considerations in AI adoption, including patient privacy, informed consent, and data security. Be transparent with patients, students, and research participants about AI use. Develop clear policies for appropriate AI implementation that align with professional values and standards. Consider implications for equity and access, ensuring AI doesn't exacerbate existing healthcare disparities. Establish ethical review processes for AI applications, particularly when handling sensitive data or making significant decisions.
Policy development and standards
Develop comprehensive policies, guidelines, and standards for responsible AI use in physiotherapy. Professional organizations should create position statements and ethical frameworks to guide implementation. Regulatory bodies should establish adaptive frameworks that address AI-related risks without stifling innovation. Educational programs need clear policies on AI use in assessment, while research requires transparent reporting standards for AI methods. All policies should be regularly updated to reflect technological advancements.
Ensuring equitable access
Work to ensure equitable access to AI tools across different settings, preventing digital divides between well-resourced and under-resourced institutions or practices. Consider how AI implementation might affect various patient populations differently, and develop strategies to maintain equitable care. In education, ensure all students have equal opportunities to develop AI competencies, regardless of their technological background. Research funding should support diverse settings to prevent AI capabilities becoming concentrated only in elite institutions.
Sustainability of AI use
Consider both ecological and resource sustainability in AI implementation. Be mindful of the environmental impact of high-compute AI systems, especially when deployed at scale. Develop policies that promote energy-efficient AI use and consider the carbon footprint of different applications. From a human resource perspective, ensure AI integration doesn't lead to staff burnout through implementation fatigue or constant technological change. Create sustainable adoption models that consider workforce wellbeing, allowing adequate time for adaptation and learning. Balance technological advancement with sustainable workloads and realistic implementation timelines.
Domain-specific guidance
Clinical practice
Patient-centred implementation: Prioritize patient needs and preferences when implementing AI. Involve patients in discussions about AI use in their care, ensuring they understand how and why AI tools are being used.
Maintaining therapeutic relationships: Focus on using AI to enhance, rather than replace, the therapeutic relationship. Ensure technology doesn't create barriers between practitioner and patient.
Clinical decision support: Use AI as a decision support tool while maintaining clinical autonomy. Recognise that AI recommendations should inform, but not dictate, clinical decisions.
Administrative efficiency: Leverage AI to reduce administrative burdens, allowing more time for direct patient care and clinical reasoning.
Supervision in clinical settings: Clinical supervisors should guide the appropriate integration of AI tools while ensuring practitioners develop and maintain core skills.
Education
Curriculum integration: Incorporate AI literacy throughout physiotherapy curricula, developing new competency frameworks that address both technological and traditional skills.
Assessment innovation: Develop new assessment methods that acknowledge AI capabilities while ensuring they evaluate true student competency.
Simulation enhancement: Use AI to create more sophisticated and responsive simulation environments while maintaining opportunities for hands-on practice.
Placement considerations: Develop guidelines for appropriate AI use during clinical placements, balancing technological assistance with authentic learning experiences.
Faculty development: Support teaching faculty in developing both the technical and pedagogical skills needed to effectively integrate AI into their teaching.
Academic integrity: Establish clear expectations and policies regarding appropriate AI use in academic work, distinguishing between helpful learning applications and problematic shortcuts.
Research
Transparent methodology: Document AI methods transparently in research publications, including details about models used, training data, and limitations.
Data integrity: Maintain rigorous standards for data collection, validation, and analysis when using AI tools in research.
Hypothesis generation: Use AI as a complement to human creativity in generating research questions, not as a replacement for researcher insight.
Literature synthesis: Leverage AI for efficient literature review and synthesis, while maintaining critical evaluation of sources and findings.
Collaborative research: Use AI to facilitate collaboration across distances and disciplines, expanding research networks and perspectives.
Verification protocols: Implement processes to verify AI-generated analyses and results, ensuring research integrity and reproducibility.
Research supervision: Guide research students in balancing AI assistance with developing independent scientific thinking and methodological skills.
Open science: Promote open science practices that make AI models, methodologies, and data available for scrutiny and replication when possible.
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