AI literacy in physiotherapy is no longer an emerging topic in physiotherapy and rehabilitation. It is already embedded across assessment, triage, monitoring, documentation, and decision support.
As these systems scale, the question is no longer whether physiotherapists will encounter AI, but whether rehabilitation services understand it well enough to deploy it safely, consistently, and at scale.
For healthcare operators, insurers, and service leaders, AI literacy is no longer an individual competency. It is an organisational requirement.
Without a shared understanding of what AI can and cannot do, digital tools risk being misused, mistrusted, or over-relied upon all of which undermine care delivery rather than improve it.
Demystifying AI Literacy in Physiotherapy
AI is often discussed in extremes: either as a transformative force that will revolutionise healthcare, or as a threat to professional roles and human judgement. Neither framing is particularly helpful.
In reality, AI is best understood as a set of computational systems designed to identify patterns in data and support decisions at scale. It does not think, reason, or care. It processes information sometimes extremely well, within the limits of its design and data.
For physiotherapy and rehabilitation, this distinction matters. AI does not replace clinical reasoning. It introduces a new layer of information that clinicians and services must interpret responsibly.
Core AI Capabilities in Rehabilitation Context
While AI technologies vary widely, most applications used in rehabilitation today draw on a small number of underlying capabilities.
Machine learning enables systems to identify patterns across large datasets, supporting risk stratification, recovery prediction, and pathway optimisation.
In rehabilitation, this is most valuable at the population level helping services anticipate demand, identify patients at risk of poor outcomes, and allocate resources more effectively.
Deep learning extends this capability to complex data such as images, video, and sensor signals. This underpins many advances in movement analysis and remote assessment, enabling objective measurement at a scale that manual observation alone cannot achieve.
However, this precision often comes at the cost of transparency, raising important questions about interpretability and trust.
Natural language processing allows AI systems to extract meaning from unstructured text such as referrals, clinical notes, and patient-reported symptoms. In practice, this supports triage, documentation, and reporting areas that directly affect access, efficiency, and clinician workload.
Individually, these capabilities offer incremental benefits. Their real impact emerges when they are integrated within governed digital rehabilitation systems.
Why AI Literacy Matters More as Systems Scale
As AI moves from isolated tools into core rehabilitation pathways, misunderstanding becomes a clinical and operational risk.
Overestimating AI can lead to automation bias, where outputs are accepted uncritically. Underestimating it can lead to missed opportunities for improving access and consistency. Both are problematic.
For organisations, AI literacy enables:
- clearer procurement decisions
- realistic expectations of performance
- appropriate governance and escalation
- confidence in explaining digital pathways to patients and regulators
This is particularly important in rehabilitation, where outcomes are shaped not only by biomechanics, but by behaviour, motivation, context, and therapeutic relationship.
Risks AI Literacy in Physiotherapy Must Address
Despite its promise, AI introduces real limitations that must be understood, not glossed over.
AI systems are only as reliable as the data they are trained on. If datasets lack diversity, outputs may not generalise across populations, reinforcing inequities rather than reducing them.
Complex models may also lack interpretability, making it difficult to explain or justify recommendations.
Perhaps most critically, AI can never account fully for psychosocial factors, lived experience, or therapeutic nuance.
When systems are deployed without adequate oversight, there is a risk that care becomes transactional rather than relational.
The World Health Organization has repeatedly emphasised that healthcare AI must remain under human oversight, with clear accountability for decisions influenced by algorithms. In physiotherapy, where trust and engagement are central to outcomes, this principle is non-negotiable.
From Individual Skill to Organisational Capability
Historically, digital literacy in physiotherapy focused on individual clinicians learning to use new tools. AI changes that dynamic.
Because AI influences pathways, prioritisation, and access, responsibility shifts toward organisations. Training must extend beyond functionality to include:
- understanding limitations
- recognising bias
- knowing when to challenge outputs
- maintaining clinical authority
- AI literacy therefore becomes part of governance, not just education.
Rehbox and an Infrastructure-Led Approach to AI Literacy
Rehbox is being developed with this systems perspective in mind. Rather than positioning AI as a set of features to be “used correctly,” the platform is designed to embed transparency, oversight, and clinical control into digital rehabilitation delivery.
The goal is not to make clinicians more technical, but to ensure that AI-enabled pathways remain intelligible, auditable, and aligned with professional judgement as services scale.
Looking Ahead
AI will continue to shape physiotherapy and rehabilitation, but its success will depend less on technical sophistication and more on how well it is understood, governed, and integrated.
The organisations that succeed will not be those that adopt AI fastest, but those that combine foundational understanding with strong clinical leadership and system-level accountability.
In that sense, AI literacy is no longer optional. It is a prerequisite for delivering safe, scalable, and human-centred rehabilitation in a digital era.
