UK Trends and Developments Contributed by: Amélie Chollet, Hannah Curtis and David Dennis, CMS
AI and Real Legal Risk: A Strategic Rethink for Life Sciences Introduction: the double-edged sword of AI in life sciences The life sciences sector stands at a techno- logical crossroads. AI promises to revolutionise everything from drug discovery to clinical deci- sion support, potentially saving countless lives through faster innovation and more personalised treatments. Yet alongside these opportunities lurks a complex web of legal risks that even sea- soned legal professionals struggle to fully grasp. This evolution could not come at a more chal- lenging time. As regulatory frameworks race to catch up with technological advancement, life sciences companies find themselves caught between innovation imperatives and compliance obligations. The consequences of missteps can be severe – from regulatory enforcement actions to product liability claims and reputational dam- age. This piece offers a provocative rethink of how in-house legal professionals should approach AI-related legal risks in life sciences companies. Rather than providing another framework for managing familiar risks, the article explores how AI’s unique characteristics challenge traditional approaches for legal risk assessment and con- siders what this means for how senior counsel can evolve their role in helping their organisa- tions navigate this transformed risk landscape. The Evolving Risk Landscape: Beyond Traditional Concerns Traditional legal risks in life sciences are well- mapped territory – data protection, product liability, third-party contractor management, regulatory compliance and intellectual property protection have long been core concerns. How- ever, AI integration in business operations has
the potential to fundamentally transform these familiar challenges in ways that require fresh thinking. This section examines examples that illustrate how AI is impacting these previously well-trodden territories by opening up new areas of potential legal risks. Data protection: from compliance exercise to strategic imperative AI systems thrive on data, creating significant tensions with core data protection principles. When an AI system is designed to identify novel patterns, defining the “purpose” of processing becomes increasingly challenging under tradi- tional purpose limitation requirements. This is compounded by the fundamental contradiction between data minimisation principles and AI’s inherent need for large datasets to improve per- formance and accuracy. Transparency requirements present additional hurdles, as explaining complex AI processing methodologies to patients in understandable consent language can become significantly more challenging. Many organisations strug- gle to craft clear, comprehensible explanations of how AI systems process personal data – while still meeting regulatory requirements for informed consent. Further complicating matters is the cross-border nature of many AI implementations. Training data frequently flows across jurisdictions with incon- sistent privacy regulations, which can result in compliance gaps and increased enforcement risks, which organisations must carefully navi- gate.
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