The guidance reflects real transactional activity in AI-pharma partnerships, particularly licensing arrangements where pharmaceutical companies fine-tune third-party AI models using proprietary data. While A&O Shearman does not name specific clients, the market involves biotech startups, established pharma giants, and AI platforms navigating secondary data use without explicit patient consent, regulatory audit trail requirements, and ownership questions in joint development agreements. The FDA's AI/ML framework now explicitly demands data provenance documentation for regulatory submissions, adding enforcement teeth to what was previously best practice.
Attorneys advising on pharma-AI transactions should flag three exposure areas: first, whether datasets include electronic health records or real-world evidence lacking proper patient authorization; second, whether collaborating parties have aligned on data ownership and audit responsibilities; and third, whether the AI model's training data can withstand FDA scrutiny or trigger HIPAA liability. With 86 percent of researchers expressing concerns about AI errors in drug discovery, and regulatory frameworks tightening around model transparency, data provenance is no longer a technical detail—it is a deal-breaker and a litigation risk.