A recent article in The Guardian (1) reports that England’s first AI‑run physiotherapy clinic, operated under the NHS, has dramatically reduced the waiting list for back‑pain patients, cutting it by half. Although the original article explores efficiency and patient outcomes, it opens the door to a much larger conversation; one that Medicas, as a medical malpractice specialist, looks to explore.

The Blurred Lines: Clinical vs. Technical Error

AI’s integration into clinical workflows undeniably heightens efficiency, but it also raises pivotal liability questions:

Algorithmic misdirection: If an AI prescribes an inappropriate exercise regimen, resulting in patient harm, who is at fault? Is it a medical negligence issue (akin to traditional clinician error), or should it be considered a technical or cyber failure? Could the algorithm itself be viewed as a defective product, invoking product liability?

Who Bears the Responsibility?

Several parties could share or shift liability:

1. Healthcare provider: The NHS (or its contracted facility) holds a duty of care to the patient. Yet, reliance on algorithmic decisions complicates whether it’s classified as “clinical oversight” or “delegated error.”

2. Software developer/coder: If the harm stems from a flawed model or faulty data-driven logic, liability could shift toward the technical creator under professional negligence or product liability frameworks.

3. Platform/system vendor: In case of systemic bugs, vendor-level defects could trigger commercial liability under service‑level agreements, especially if marketed as “safe for clinical use.”

When ‘Glitches’ Steer the Harm, How Clear Is Fault?

Glitches, by nature, are opaque. As AI systems evolve via machine learning models and continuous updates, tracing the root cause becomes a forensic challenge:

1. Attribution becomes murky: Was the harm due to a one-off system bug, sloppy implementation, or faulty clinical judgment augmented by AI?

2. Proof burden increases: Claimants may find it difficult to demonstrate causation without access to proprietary algorithms, logs, or data records.

3. Regulatory requirements: As AI systems come under stricter regulatory scrutiny (e.g. UK’s proposed AI Act, medical device MDR), insurers must prepare for complex investigations and multisectoral claims.

Implications for Future Insurance Solutions

This new frontier demands innovation in how insurance responds to AI‑enabled healthcare:

1. Hybrid coverage models
Traditional medical malpractice coverage may need complementary technology‑E&O (Errors & Omissions) or product liability extensions to fully fit within AI‑driven clinical models.

2. Data‑driven underwriting
Premium modelling must evolve to incorporate algorithmic risk factors, such as model accuracy, update frequency, audit logs, transparency, and developer credentials.

3. Risk mitigation through certification
Incentivising or requiring third‑party AI certification (e.g., algorithmic safety, clinical validation) could reduce premiums and improve patient trust.

4. Claims handling & forensic readiness
Policies should include clauses granting insurers access to system audit trails and logs, enabling thorough claims investigations and expert assessment.

Conclusion

The AI‑run NHS physio clinic may well mark a strategic inflection point, not only for healthcare delivery but also for risk management and insurance. At Medicas, our expert brokers are dedicated to ensuring that our policies meet the specific requirements of our clients.

As a specialist in the healthcare sector, we not only structure policies, but also provide technical advice around key exposures that may affect the business of our clients.

To learn more, please get in Contact.

Sources:
The Guardian (2025) First NHS AI-run Physio Clinic in England Halves Back-Pain Waiting List: https://www.theguardian.com/so...