Sponsored content: Craig Mounser, practice leader for medical technology and life sciences at Travelers Europe, explains how businesses can benefit from AI opportunities while managing risks
Artificial intelligence (AI) is transforming 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 trialled for early cancer detection, and in medtech firms developing personalised healthcare solutions.
But with opportunity comes responsibility. As AI ushers in life-changing advances, it introduces complex risks that businesses must mitigate in order to thrive in this rapidly evolving space.
Problems with AI
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 General Data Protection Regulations (GDPR).
Finally, explainability – 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
Fortunately, protective strategies exist. For one, AI should support, 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 so users can make informed decisions about what AI will – and won’t – do.
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 contractual risk transfer mechanisms as standard operating procedure to mitigate professional indemnity, cyber and product liability exposures arising from artificial intelligence deployments.
AI’s potential in medtech and life sciences is extraordinary, but the risks are just as significant.
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.
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