Michael Rowe

Trying to get better at getting better

Here is the recording of our the focused symposium we presented at the IFOMPT conference, on Generative AI in Physiotherapy. The session was co-facilitated by Doris Chong, Emmanuelle Opsommer, and Cliffton Chan.

Here are the slides.

The symposium built on the pre-conference survey and workshop, and is the penultimate step in our collective sense-making of the impact of generative AI in physiotherapy. We will now take what we learned through this series of events, and use it to draft a discussion document to be shared with the community.

Let us know if you’d like to be notified when the discussion document is available for public comment.


Key points from the session include the following (this is a short version of what’s in the slides):

Generative AI overview

  • Advanced language models capable of human-like outputs
  • Multimodal capabilities (analyzing images, generating audio, processing video)
  • Ability to perform complex programming tasks
  • Large context windows for processing vast information
  • Emphasis on human-AI collaboration rather than replacement

AI series process

  • Pre-conference survey across 16 countries
  • In-conference survey with 68 responses
  • Pre-conference workshop exploring AI implications
  • Focused symposium for ongoing dialogue
  • Discussion document on integration strategies

Key survey findings

  • Clinical practice: 83% believe AI will impact diagnosis and assessment most
  • Education: 76% identified AI literacy as crucial for physiotherapists
  • Research: 89% indicated AI could enhance access to up-to-date research

Clinical practice

  • Capabilities: Enhanced diagnosis, personalised treatment planning, real-time monitoring
  • Opportunities: Improved efficiency, patient engagement, early diagnosis
  • Risks: Potential misdiagnosis, privacy concerns, depersonalisation of care
  • Strategies: Develop guidelines, prioritise ongoing education, maintain human touch

Physiotherapy education

  • Capabilities: Personalised learning, virtual simulations, automated assessment
  • Opportunities: Adaptive learning paths, improved practical training, global collaboration
  • Risks: Potential loss of critical thinking skills, over-simplification of topics
  • Strategies: Integrate AI literacy into curricula, blend AI with traditional teaching methods

Research

  • Capabilities: Rapid literature review, advanced data analysis, hypothesis generation
  • Opportunities: Accelerated research processes, enhanced interdisciplinary collaboration
  • Risks: Potential bias in AI-generated hypotheses, data privacy concerns
  • Strategies: Develop clear guidelines, maintain human oversight, foster collaboration

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