SupplyLogic

Medical device manufacturers have moved beyond asking whether generative models can draft text. The more consequential question is how these models will operate within design control, clinical evidence generation, regulatory affairs, quality systems, and post-market surveillance without weakening accountability. Over the next three to five years, the winners will not be the companies with the most pilots. They will be the manufacturers that connect artificial intelligence to governed product data, validated workflows, and named decision owners while preserving the traceability expected under ISO 13485, ISO 14971, 21 CFR Part 820, and regional market-authorization regimes. The emerging discipline of Generative AI in MedTech therefore needs to be understood as a lifecycle capability rather than a standalone writing assistant. Its value will come from reducing the friction between user needs, design outputs, verification evidence, clinical conclusions, submission content, manufacturing r...

Generative AI Use Cases in research-based biopharma are moving beyond isolated copilots and into the scientific and quality systems that govern how molecules become medicines. Over the next three to five years, the important change will not be a single dramatic breakthrough. It will be the gradual connection of target biology, medicinal chemistry, clinical evidence, safety surveillance, regulatory documentation, and manufacturing knowledge through models that can propose, summarize, simulate, and explain. For companies operating at the scale of Pfizer, Roche, or Novartis, that shift could compress decision cycles while preserving the traceability expected in GxP environments. The most consequential Generative AI Use Cases will therefore be judged by more than model fluency. They must improve a defined scientific or operational decision, remain grounded in controlled evidence, and fit established accountabilities. A generated protocol synopsis, for example, is useful only when clinical...