AI Risks in Medical Technology and Life Sciences
Artificial intelligence (AI) is revolutionising medical technology in ways that once seemed confined to science fiction. From helping clinicians detect diseases earlier, to powering wearable health devices that monitor patient wellbeing in real time, AI has quickly become central to innovation. In the UK, we’re seeing this across the NHS, where AI tools are being used in trials for early cancer detection and rapid diagnosis of heart conditions. Medtech firms are developing AI-driven healthcare solutions that range from physiotherapy apps to a tool that helps cancer patients plan more precise radiotherapy regimens tailored to their specific needs.
But with opportunity comes responsibility. As AI ushers in life-changing advances, it introduces complex risks that businesses must mitigate to thrive in this rapidly evolving space.
Problems with AI in healthcare
AI has generated new layers of risk when compared with traditional software. Its accountability, safety, reliability, bias, security, privacy and explainability have raised concerns within the UK’s Information Commissioner’s Office (ICO) and the Medicines and Healthcare products Regulatory Agency (MHRA).
Diagnostic AI is one example. A model trained on incomplete or non-representative datasets could misdiagnose certain conditions or misclassify others, potentially delaying treatment. Similarly, wearable health devices powered by AI, like those tracking heart irregularities, risk causing widespread false alarms if inadequately tested — overwhelming clinicians and eroding patient trust.
Bias is particularly pressing. In the absence of diverse, quality training data, AI systems may unintentionally disadvantage underrepresented groups, generating unequal health outcomes. Security and privacy concerns also loom large over the vast amounts of sensitive health data medtech firms collect. Breaches can generate severe regulatory and reputational consequences under UK GDPR.
Finally, the explainability of and AI result — or lack thereof — is a growing challenge. If clinicians can’t understand how an AI model reached a diagnosis, patient trust falters and regulators are less likely to approve its use.
Risk mitigation strategies for AI in life sciences, healthcare and medtech
Fortunately, protective strategies exist. For one, AI should support, but not replace, clinicians and decision-makers. Rigorous human validation is essential to maintaining quality of care in higher-risk applications like medical diagnosis. The UK government’s policy paper AI Action Plan for Justice stresses the need for “meaningful human control” over AI-driven decisions.
Transparency and explainability are equally important. AI developers should document model design, training data, and known limitations enabling users to make informed decisions about AI capabilities and constraints.
Continuous testing and evaluation are critical too. This includes simulating diverse patient populations, stress-testing cases, and monitoring real-world performance after deployment.
Lastly, organisations should implement standard contractual risk-transfer mechanisms to mitigate Professional Indemnity, Product Liability and Cyber exposures arising from artificial intelligence deployments. For example, Professional Indemnity insurance provides cover for breach of contract and/or third-party financial losses arising from AI software errors. Contractual risk transfer should clarify the responsibilities of all parties involved and ensure that any financial loss does not come back to the AI provider.
The same applies in relation to bodily injury. The contract should make it clear that the final diagnosis is to be made by a medical professional (and not the AI medical device). This means that in the event of a false negative or false positive generated by the AI, the resulting bodily injury exposure cannot land with the AI device provider.
Cyber cover can protect against the risk of third-party data not being adequately protected – or being unintentionally shared – as a result of an AI software error.
Risk transfer and the future outlook
Many organisations rely on third-party vendors for AI solutions as a way to minimise their own risk. They may assume that outsourcing development means they aren’t accountable for errors or harmful outcomes. In reality, companies that adopt AI-powered tools remain responsible for how those systems perform and the impact they create. What’s more, vendors are often reluctant to disclose much about how their technologies work. As a result, business owners may face greater liabilities than they expect. Having adequate protection against those liabilities places them in a stronger position to innovate.
AI’s potential in medtech and life sciences is extraordinary, but so are the risks. Businesses with strong protective frameworks around AI risks will more capably maintain patient trust, regulatory compliance, and resilience. They will be poised to deliver the life-changing advances that AI promises.
