Generative AI in MedTech: Lessons From Real Implementation Work
My first serious encounter with Generative AI in MedTech did not begin with a dramatic diagnostic breakthrough. It began with an overworked design assurance lead, a conference room covered in traceability printouts, and a product team trying to reconcile user needs, risk controls, verification protocols, and submission commitments before a design review. The model produced a polished summary in minutes. It also quietly merged two distinct hazards and attributed a verification result to the wrong device configuration. That experience captured the promise and the danger of the technology: it can compress days of specialist effort, but fluency is not evidence. Since then, I have worked through use cases spanning research and product development, regulatory affairs, quality management systems, and post-market surveillance. The most useful way to understand Generative AI in MedTech is not as a universal automation layer but as a controlled capability embedded in defined workflows. The succ...