As healthcare and biopharma companies race to integrate artificial intelligence (AI) into drug development, clinical trials, and diagnostics, insurance underwriters reassess the industry’s evolving risk landscape, identifying new opportunities to tailor coverage for a rapidly transforming sector.
The shift coincides with the advancement of AI-driven precision medicine. Breakthroughs in biomarker mapping and gene sequencing have enabled earlier disease detection and more personalized treatment plans.
Life science firms are now combining these breakthroughs with AI platforms capable of analyzing vast datasets, helping them develop more sophisticated therapeutics while improving patient outcomes and extending lifespans.
Jim Craig, senior vice president of underwriting at Munich Re Specialty in North America, said the expansion reflects the rapid evolution of the market. “Where data is more accessible, risk selection becomes more accurate and sophisticated.”
AI speeds up clinical trials, but adds new liabilities
AI is already reshaping how clinical trials operate. Algorithms can analyze electronic medical records (EMRs) to identify patients who are more likely to qualify for, and benefit from, trials. Craig said this reduces recruitment delays, a persistent challenge that often forces companies to extend timelines or over-insure due to inaccurate enrollment forecasts.
Beyond recruitment, AI models can sift through scientific literature to identify promising therapeutic compounds and predict drug efficacy. In diagnostics, the technology now interprets medical images with accuracy comparable to that of expert radiologists in specific settings.
Rising risks: Overdiagnosis, bias, cyber threats
However, the same capabilities that make AI appealing also introduce high-stakes risks. Underwriters are watching for overdiagnosis, which can cause unnecessary treatment or patient anxiety when algorithms produce inaccurate results.
It also increases the risk of unintentional bias, particularly when AI-driven targeting favors specific populations without a clear medical justification.
Cybersecurity remains another critical concern. As life science organizations collect and analyze vast volumes of sensitive data, they become prime targets for cyberattacks, making information security a core underwriting issue.
Coverage evolves: From product liability to AI “hallucinations”
Insurers are expanding their coverage models. While traditional products, such as medical product liability and errors and omissions (E&O), remain core, new solutions emerge. For example, Munich Re’s aiSure policy addresses AI model errors, including generative AI “hallucinations,” which can cause lost revenue, business interruption, or legal exposure.
The urgency is rising as AI adoption accelerates. A global SAS study found that nine in ten insurers plan to invest in generative AI by 2025. Grand View Research estimates that the AI in healthcare market could surge to $505.59 billion by 2033, reflecting a rapid expansion and growing reliance on AI-enabled care.
Regulators are also moving quickly. The U.S. Food and Drug Administration has received over 300 submissions incorporating AI components and continues to develop frameworks for the credible use of AI across the drug lifecycle.
For insurers, the message is clear: AI is transforming life sciences, and underwriting must evolve just as fast.
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