Nelson Advisors: What does the Chartered Society of Physiotherapy's warning on 'AI Physiotherapy' mean for the NHS, Founders and Investors?
Introduction: a warning that was always coming
For the better part of a year, the story of AI physiotherapy in the NHS has been told almost entirely in the language of momentum. A Cambridge start-up, Flok Health, secured Class IIa medical device approval under EU regulations in October 2025, becoming the first AI system in Europe cleared to deliver a full care pathway, including diagnostic decisions, without a human clinician supervising each case. The clearance initially covered back pain and sciatica. In August 2026 it was extended to cover all musculoskeletal and pelvic health pathways, taking in hip and knee problems as well as urinary incontinence. Between those two regulatory milestones the company expanded across eleven NHS regions, raised an oversubscribed $12.5 million Series A led by AlbionVC, and reported that more than 80 per cent of patients in one English rollout rated the AI clinic as good as or better than in-person physiotherapy.
That is the version of the story told by press releases and funding announcements. This week, the professional body for physiotherapists in the UK offered a different version.
The Chartered Society of Physiotherapy (CSP), speaking to Digital Health (1) urged caution over the use of autonomous AI for the assessment and treatment of NHS patients. Its central argument, delivered by Helen Sharma, head of practice improvement at the CSP, is deceptively simple: a medical device being approved to perform a specific diagnostic or clinical function should not be confused with the autonomy of a healthcare professional.
(1) https://www.digitalhealth.net/2026/09/physiotherapy-leaders-warn-ai-could-compromise-patient-safety/)
"Physiotherapists are autonomous practitioners because they are professionally regulated, accountable for their decisions and responsible for the care they provide," Sharma said. "Safe and effective physiotherapy depends not only on diagnosis, but on clinical judgement, ethical reasoning, communication, shared decision making and responding to an individual's unique circumstances. These essential aspects of care remain beyond the scope of medical device regulation and cannot be replicated by AI."
She went further. Although AI has potential to improve patient care and support physiotherapists, she argued, without appropriate safeguards it could widen existing health inequalities, compromise patient safety and undermine public confidence in healthcare services. "A major concern," she added, "is the limited evidence regarding the safety, effectiveness and real world impact of many AI enabled technologies in physiotherapy."
It would be easy to read this as a professional body defending its turf. The CSP has not called for a ban. It published its own principles on the use of AI in physiotherapy in March 2025, describing them as a guideline for the future rather than a barricade against it, and committed to reviewing them every six months for two years precisely because it expected the technology to move quickly. What the CSP has done is draw a line between two things that the market, the media and to some extent the regulators have been allowing to blur: the technical clearance of a device and the professional autonomy of a clinician.
That distinction matters enormously and not only for physiotherapists. It goes to the heart of how the NHS will adopt autonomous AI across every community pathway over the next decade, how founders should design and evidence their products, and how investors should price the regulatory and reputational risk of companies that are, in effect, asking to be treated as clinicians.
This article sets out what actually happened, what the two sides are really arguing about, where the evidence stands, what safeguards would look like in practice and what it all means for the people who commission, build and fund this technology.
Part one: what Flok Health was actually approved to do
To understand the disagreement, it helps to be precise about the approval.
Under the EU Medical Device Regulation, software that provides information used to make diagnostic or therapeutic decisions is classified according to the severity of what could go wrong if that information is incorrect. Class IIa sits in the middle of the risk hierarchy. It is the classification typically applied to software whose decisions could lead to a moderate deterioration in a patient's health or a surgical intervention, as distinct from Class IIb or III, where the consequences of error are serious harm or death. Achieving Class IIa requires a notified body to audit the manufacturer's quality management system and technical documentation, including its clinical evaluation and it obliges the manufacturer to run post-market surveillance for as long as the product is on the market.
What made Flok's October 2025 clearance notable was not the class but the intended use. The company's device was approved to triage, diagnose, treat and discharge patients along a defined pathway without a clinician reviewing each individual case. That is a materially different intended use from most digital MSK products, which are cleared as decision support, exercise delivery or triage tools with a human clinician retaining the diagnostic decision. Flok has also, separately, been registered with the Care Quality Commission as a healthcare provider, which the company describes as unique among digital musculoskeletal services.
In August 2026, the scope of the clearance was extended from back pain and sciatica to all musculoskeletal and pelvic health community pathways. In practical terms this took the product from a single, relatively well-defined condition into hip and knee pain, where the differential diagnosis is broader and the interface with orthopaedic surgery is more consequential, and into pelvic health, where the patient population is overwhelmingly female, the conditions are heavily stigmatised, and the clinical assessment has traditionally depended on a degree of trust and physical examination that is hard to replicate through a screen.
The commercial case is not hard to see. Low back pain is the leading cause of disability globally. More than 390,000 people were sitting on musculoskeletal waiting lists in England at the time of Flok's Series A. The company said its expanded pathways would address conditions affecting more than 20 million people in the UK each year, and it reported saving an average of 856 clinical hours per month at a single NHS trust. Against that backdrop, an AI clinic with no waiting list is not a novelty; it is a direct answer to one of the NHS's most persistent operational failures.
So the regulators have said the device is safe enough, on the evidence presented, for the intended use. The CQC has said the organisation is fit to provide care. NHS regions have said yes. Investors have said yes. Patients, at least those who completed the surveys, have largely said yes.
What, then, is the CSP objecting to?
Part two: two meanings of "autonomous"
The CSP's argument turns on a word that has been doing an enormous amount of work in this story.
When Flok and its backers describe the system as autonomous, they mean it in the engineering and regulatory sense: the software executes its clinical function without a human in the loop for each decision. The device has been shown, to a notified body's satisfaction, to do that within acceptable risk bounds for its intended use.
When physiotherapists describe themselves as 'autonomous practitioners', they mean something different. Physiotherapy in the UK has been an autonomous profession since 1977, when the Department of Health confirmed that physiotherapists could assess and treat patients without medical referral. That autonomy is not a property of the physiotherapist's skill. It is a property of the system around them: registration with the Health and Care Professions Council, a code of conduct, mandatory continuing professional development, a duty of candour, professional indemnity, the possibility of being struck off and a personal, named accountability for every decision made about every patient.
Sharma's point is that these two meanings are being conflated and that the conflation is dangerous because it invites the public, commissioners and policymakers to assume that a device cleared to make a decision is equivalent to a professional who is accountable for it.
The distinction is sharper than it first appears. Consider what happens when something goes wrong. If a registered physiotherapist misses a cauda equina syndrome, a rare but devastating condition in which compression of the nerves at the base of the spine can cause permanent paralysis and incontinence if not treated within hours, there is a clear chain of accountability. The clinician is personally answerable. Their employer is vicariously liable. The regulator can investigate. The patient can complain, and the complaint will be heard by people who understand what a reasonable clinician should have done.
If an autonomous AI misses the same condition, the chain is much less clear. Is the manufacturer liable as the producer of a defective device? Is the NHS trust liable as the provider that chose to deploy it? Is the CQC-registered provider liable as the entity delivering care? Was the failure a design defect, a data quality problem, an edge case outside the intended use, or a patient who did not answer the safety-netting questions accurately? Every one of these has a different legal answer and none of them gives the patient the thing that professional regulation was designed to guarantee: a named, accountable person who owed them a duty of care.
This is what Sharma means when she says that clinical judgement, ethical reasoning, communication and shared decision-making "remain beyond the scope of medical device regulation." The MDR was written to assure that a device does what it says on the label. It was not written to answer the question of who is responsible for a patient's care, and it does not attempt to.
None of this means the CSP is right that AI "cannot replicate" these aspects of care. That is a stronger claim and one that reasonable people will argue about for years. But the narrower claim, that device clearance and professional accountability are different things and should not be described as if they were the same, is very hard to dispute.
Part three: the evidence question
Sharma's second major concern is evidence: specifically, "the limited evidence regarding the safety, effectiveness and real world impact of many AI enabled technologies in physiotherapy."
It is worth being fair to both sides here.
Flok is not evidence-free. It has published outcome data, it has run its service within real NHS pathways across multiple regions, and it has satisfied a notified body's clinical evaluation requirements. The company's reported figure that more than 80 per cent of patients rated the service as good or better than in-person care is a real data point. The 856 clinical hours saved per month at one trust is a real operational outcome. These are not nothing.
But they are also not what the physiotherapy profession, or indeed the wider clinical research community, would recognise as strong evidence of safety and effectiveness. Patient satisfaction is a measure of experience, not of clinical outcome. Hours saved is a measure of efficiency, not of whether the right patients were treated in the right way. Neither tells you the rate at which serious pathology was missed, how many patients were inappropriately discharged, how many later presented to A&E or to a GP, or how outcomes compared with a matched cohort receiving standard care.
The gold standard for that kind of question remains the randomised controlled trial, ideally with an independent evaluation and a pre-registered protocol. In its absence, the next best evidence is large-scale, transparently reported real-world data with clear denominators: how many patients entered the pathway, how many were escalated, how many were discharged, what happened to them afterwards, and how the numbers compare with the service the AI replaced.
There is a structural reason why this evidence is thin, and it applies well beyond Flok. Medical device regulation, both in the EU and under the UK's evolving framework, requires clinical evaluation proportionate to risk, but for a Class IIa software device that evaluation can lean heavily on literature, equivalence to existing devices, and post market data rather than prospective trials. The regulation is designed to get safe devices to market at a reasonable cost. It was never intended to substitute for the kind of comparative effectiveness research that the NHS uses to decide whether an intervention should be commissioned.
This creates a gap that the CSP has correctly identified. A device can be lawfully on the market and lawfully in use in the NHS while the question of whether it is as safe and effective as the care it replaces remains, in the strict scientific sense, open. NICE's evidence standards framework for digital health technologies exists to fill this gap, and it sets a considerably higher bar for products that make diagnostic decisions than for those that deliver information or exercise programmes. Whether every NHS region that has adopted an AI physiotherapy service has demanded evidence at that level is a fair question, and one that commissioners should be prepared to answer.
The August 2026 extension of scope sharpens this concern. Evidence that a system safely manages low back pain does not automatically transfer to hip pain, knee pain or pelvic floor dysfunction. The red flags are different. The differential diagnoses are different. The population is different. A rigorous approach would treat each new pathway as a new clinical claim requiring its own evidence, and the profession is entitled to ask whether that has happened or whether the extension rested largely on the regulatory logic that the underlying software architecture is the same.
Part four: where the safety risk actually lives
Sharma's warning that AI "could compromise patient safety" deserves to be made concrete, because the risk is not evenly distributed across the patient population. It clusters in specific, predictable places.
The first is serious pathology masquerading as routine MSK complaint. Every physiotherapist is trained to screen for red flags: cauda equina syndrome, spinal infection, malignancy, inflammatory arthropathy, fractures in the osteoporotic patient, referred pain from visceral disease. These are rare, which is exactly the problem. A system trained on the overwhelming majority of benign presentations may perform superbly on average while being least reliable on the cases where being wrong is catastrophic. The question for any autonomous MSK system is not its overall accuracy but its sensitivity for the rare, high-consequence cases, and how it behaves when a patient's answers are ambiguous, incomplete or contradictory.
The second is the patient who does not fit the model. Physiotherapists routinely see people with multiple long-term conditions, cognitive impairment, chronic pain with significant psychological overlay, language barriers, or a history that makes the standard pathway inappropriate. A human clinician adapts. An autonomous system either escalates, which is the right outcome only if the escalation path is fast and well-staffed, or proceeds with a plan that was designed for someone else.
The third is the failure of communication. Sharma places communication and shared decision-making at the centre of safe physiotherapy, and this is not sentimentality. A large proportion of physiotherapy's effectiveness for persistent pain depends on the patient understanding their condition, believing that movement is safe, and adhering to a programme over weeks. That is a relational outcome. An AI clinic may achieve it for many patients, and the satisfaction data suggests it often does, but there will be a cohort for whom it does not, and the system needs to detect that cohort rather than record them as non-adherent and discharge them.
The fourth is the quieter risk of over-treatment and over-reassurance. Autonomous systems optimised for throughput can drift towards discharging patients who report improvement without verifying it, or towards prolonging episodes of care that a clinician would have closed. Neither shows up as a safety incident. Both erode the value of the service.
None of these risks is unique to AI. Human physiotherapists miss red flags, misjudge complex patients, communicate badly and over-treat. The difference is that a human error is idiosyncratic, while a system error is systematic. If a flaw exists in the pathway logic, it does not affect one patient; it affects every patient who presents in that way, across every region where the system is deployed, until someone notices. That is the strongest argument for the CSP's insistence on safeguards, and it is an argument that applies with more force the wider the scope of the clearance becomes.
Part five: health inequalities and the digital front door
The most under-examined part of Sharma's statement is her warning that AI without safeguards "could widen existing health inequalities."
Musculoskeletal conditions are not evenly distributed. They fall disproportionately on people in manual occupations, on older people, on people in deprived areas, and on people with comorbidities. The same populations are, on average, less likely to have reliable broadband, a recent smartphone, a private space to complete a video assessment, high digital literacy, or English as a first language. They are also more likely to have the complex, multi-morbid presentations that autonomous systems handle least well.
An AI physiotherapy service with no waiting list is a substantial benefit to the patient who can use it. But if the patients who cannot use it are the same patients who most need care, then the effect of the service on inequality depends entirely on what happens to them. If they are routed to a well-resourced, in-person service with the capacity freed up by the AI, inequality narrows. If the in-person service is quietly shrunk because the AI now handles the volume, the digitally excluded are left with a slower, thinner service than they had before, and inequality widens.
This is a commissioning decision, not a product flaw. But it is precisely the kind of decision that gets made by default when a technology is described as autonomous, because autonomy implies that the human service is no longer needed. The CSP's insistence that AI should "support physiotherapists" rather than replace them is, read charitably, an argument about system design rather than professional protectionism: keep the human capacity, redirect it towards the people the machine cannot serve, and measure the outcomes of both groups.
There is also a subtler equity issue in the data itself. If a system is trained and validated primarily on the patients who successfully complete a digital pathway, it will be optimised for them. Its performance on the patients who drop out, who are disproportionately those with the greatest need, will be unknown. Any serious evaluation of AI physiotherapy should report outcomes stratified by deprivation, age, ethnicity and digital access, and should report what happened to the patients who started and did not finish.
Part six: what the pro adoption case gets right
It would be a mistake to present this as a debate with one reasonable side. The argument for autonomous AI in community MSK pathways is strong and the CSP itself does not dispute that AI has potential to improve care.
The NHS's MSK problem is fundamentally one of capacity. With hundreds of thousands of people waiting, the counterfactual to an AI assessment is very often not a prompt assessment by a registered physiotherapist. It is a wait of weeks or months, during which acute pain becomes persistent pain, work is lost, mental health deteriorates, and the eventual treatment becomes harder and more expensive. Against that counterfactual, a system that can see a patient within a day and safely manage the majority of straightforward presentations is a clear net benefit, even if it is not as good as a human clinician seen promptly.
The regulatory clearance is also more meaningful than critics sometimes allow. A Class IIa clearance for an autonomous care pathway is not a rubber stamp. It requires a documented risk management process, a clinical evaluation, a quality management system, and an ongoing obligation to collect and act on post-market data. CQC registration adds a further layer of inspection that most digital health companies never face. Flok has voluntarily submitted itself to more scrutiny than the vast majority of the app-based MSK products that have been quietly used in NHS pathways for years with far less oversight and far less evidence.
There is a version of the safety argument that, taken to its conclusion, would prevent any autonomous system from ever being deployed because no system can be shown to be safe in every edge case in advance. That is not a serious position, and the CSP has not taken it. The profession's own AI principles accept that the technology will be used and seek to shape how. The productive debate is not whether autonomous AI physiotherapy should exist but what conditions should attach to it.
Part seven: what safeguards would actually look like
If the CSP's concerns are legitimate and the case for adoption is real, the question becomes practical. What would "appropriate safeguards" mean in a deployed NHS pathway?
Drawing on the CSP's principles, NICE's evidence standards framework, and the experience of other autonomous clinical AI deployments, a reasonable set of conditions might include the following.
Clear and public reporting of pathway outcomes. Every NHS region deploying an autonomous MSK service should be able to state, for each pathway, how many patients entered, how many were escalated to a human, how many were discharged, how many re-presented within a defined period, and how many serious adverse events or missed diagnoses were identified. These numbers should be published in a form that allows comparison with the standard pathway. The absence of such reporting is the single largest gap between what the market currently offers and what the profession is asking for.
Named clinical accountability at the provider level. If the device is autonomous, the provider is not. A CQC-registered provider delivering autonomous AI care should have a named registered clinician, with appropriate indemnity, who is accountable for the clinical governance of the pathway: for reviewing escalations, for auditing a sample of discharges, for investigating incidents, and for deciding when the system's scope should be narrowed. This does not put a human in the loop for every decision, which would defeat the purpose. It puts a human in the loop for the system.
Independent evaluation of each new pathway. Extending an autonomous system from back pain to pelvic health is a new clinical claim. Each such extension should be accompanied by an evaluation, ideally independent of the manufacturer, that reports on the specific red flags and failure modes of that pathway. Commissioners should ask for this before adoption, not after.
Guaranteed and measured access for the digitally excluded. Every autonomous digital pathway should have an explicit, funded route for patients who cannot or will not use it, with the same or better waiting times as the digital route. Outcomes for that group should be reported alongside the digital cohort.
Transparency with patients. People should know when they are being assessed by an AI system rather than a human, what the system can and cannot do, how to reach a human clinician, and what happens to their data. This is a matter of consent as much as of confidence, and the CSP is right that public confidence in healthcare services is not a resource to be spent casually.
Contractual triggers for scope reduction. Commissioning contracts should include explicit thresholds, such as a missed diagnosis rate or an unexpected re-presentation rate, above which the autonomous scope is paused and the pathway reverts to clinician-supervised operation while the cause is investigated. This is the digital equivalent of the clinical governance processes that every NHS trust already applies to its human workforce.
None of these is exotic. Several are almost certainly already in place in some form for the more sophisticated deployments. The point of stating them is that the profession's warning is more useful as a checklist than as a headline, and commissioners who can demonstrate that they have addressed each item are in a very different position from those who cannot.
Part eight: implications for NHS commissioners
For integrated care boards and trusts, the CSP's intervention changes the risk calculus of adopting autonomous MSK AI in three ways.
First, it raises the reputational stakes. A serious incident involving an autonomous AI pathway will now be reported in the context of a professional body having publicly warned that exactly this could happen. Commissioners who adopted without demanding evidence and safeguards will find that context uncomfortable.
Second, it creates a clear governance expectation. The CSP has effectively told its members what it expects of them when working alongside AI systems, and its members are the people who will be asked to take responsibility for pathway governance, to review escalations, and to pick up the patients the system cannot manage. Commissioners who want physiotherapists to do that work will need to engage with the profession's concerns rather than dismiss them.
Third, it points towards the questions commissioners should be asking of any vendor: not "is it approved?" but "what is the evidence for this specific pathway, in a population like ours, and what happens to the patients it cannot serve?" The vendors with good answers to those questions should welcome them being asked, because it separates them from the ones without.
Part nine: implications for founders and product teams
For companies building in this space, the lesson is that regulatory clearance is the beginning of the credibility question, not the end of it.
The market has learned to treat medical device classification as the finish line. For a decision support tool, it may be. For a product that replaces the clinician's decision, the finish line moves. The audiences that determine adoption at scale, which is to say clinicians, professional bodies, commissioners and eventually NICE, will want evidence that looks like clinical research, governance that looks like clinical governance and transparency that looks like the NHS's own reporting. Companies that build those things early will find the CSP a far more willing partner than the ones that arrive with a certificate and a satisfaction score.
There is a product design lesson too. The CSP's list of what AI cannot replicate, "clinical judgement, ethical reasoning, communication, shared decision-making and responding to an individual's unique circumstances," is also a list of where an autonomous system most needs to know its own limits. Systems that are conservative about escalation, transparent about uncertainty and generous in routing to humans will have worse throughput metrics and better safety records. In a market where the professional body is now watching, the second matters more.
Founders should also read the August scope extension as a warning about pace. Going from one condition to every community MSK and pelvic health pathway in ten months is remarkable regulatory execution. It is also the kind of speed that makes professions nervous, because it outruns the evidence base. The companies that will win the long game are the ones that expand scope at the speed of their evidence, not the speed of their regulatory counsel.
Part ten: implications for investors
For investors, the CSP's warning is not a red flag against the category. It is a repricing signal for a specific kind of risk.
Autonomous clinical AI companies have been valued, in part, on a thesis that regulatory clearance is a durable moat. Flok's clearance is a real achievement and it does confer a first mover advantage, but the CSP has just demonstrated that professional acceptance is a separate and independently necessary condition for scale. A product can be legally deployable and commercially marketable while still facing resistance from the workforce it depends on for escalations, governance and referrals. That resistance is not fatal, but it slows adoption, raises the cost of sale and increases the probability that a single adverse event becomes a category-defining story.
The due diligence questions this suggests are specific. What is the evidence base for each cleared pathway, and is any of it independent? What are the escalation rates, and how are escalations handled? What is the missed serious pathology rate, and how is it measured? What is the relationship with the relevant professional bodies, and has the company engaged with the CSP's principles? What are the contractual terms with NHS customers around incidents and scope reduction? What happens to the company's growth model if a commissioner demands clinician review of every discharge?
There is also an M&A angle. Established MSK and digital health players who have grown cautiously through decision-support and clinician-supervised models will look at the autonomous entrants with a mixture of envy and unease. The natural outcome, as the evidence and governance questions mature, is consolidation: autonomous technology acquired into organisations that already have the clinical governance infrastructure, the workforce relationships and the commissioning credibility to deploy it safely. Investors should think about which side of that transaction their portfolio companies will be on, and price accordingly.
The broader point is that the CSP has just done something useful for the market. It has articulated, publicly and precisely, what the profession will require before it treats autonomous AI as a partner rather than a threat. That list is now a roadmap. The companies that follow it will be worth more. The ones that ignore it will find that a Class IIa certificate is a weaker asset than their pitch deck assumed.
Conclusion: the line between a device and a clinician
The CSP's intervention will be characterised in some quarters as a profession resisting change. It is more accurately read as a profession asking that change be described honestly.
Flok Health has built something real. It has cleared a regulatory bar that no other European company has cleared, it is delivering care to NHS patients who would otherwise be waiting, and it has attracted serious capital on the strength of that achievement. None of that is in dispute. What is in dispute is the word "autonomous," and whether the public, the NHS and the market understand the difference between a system that is permitted to make a decision and a professional who is accountable for it.
Helen Sharma's argument is that the difference is fundamental, that it is not addressed by medical device regulation, and that pretending otherwise risks patient safety, health equity and public trust. On the evidence, she is right on all three counts, and the fact that she is right does not mean autonomous AI physiotherapy should stop. It means it should be evaluated like a clinical service, governed like a clinical service, and described to patients as what it is.
The next phase of this story will not be decided by regulators, who have already spoken, or by investors, who have already voted. It will be decided by the evidence that emerges from the NHS pathways where autonomous AI is now operating at scale and by whether the companies and commissioners involved are willing to publish that evidence, including the parts that do not flatter them. The CSP has set out what it wants to see. The rest of the sector should take that as an invitation rather than an obstacle.
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