Edith Heroux

Generative AI in MedTech is moving from isolated experiments into design control, regulatory documentation, clinical evidence workflows, complaint handling, and field service support. That transition changes the central question. It is no longer whether a model can produce a useful response in a demonstration; it is whether the complete system can operate predictably within a regulated medical-device environment, preserve evidence, protect sensitive data, and remain under control as products, regulations, and models change. This readiness checklist treats Generative AI in MedTech as a lifecycle capability rather than a software feature. It is intended for cross-functional teams from research and product development, design assurance, regulatory affairs, clinical affairs, quality, manufacturing engineering, supplier quality management, medical affairs, cybersecurity, and post-market surveillance. Each item includes a rationale because a checked box without a shared understanding rarely...

When our organization first attempted to integrate multiple AI systems across departments, we discovered that technological sophistication meant nothing without proper orchestration. Teams had independently deployed chatbots, predictive analytics tools, and automation frameworks, each solving isolated problems brilliantly. Yet when executives asked for consolidated insights or cross-functional workflows, we faced a fragmented landscape where AI systems couldn't communicate, share context, or coordinate actions. This painful realization launched our three-year journey into unified AI orchestration, teaching us lessons no whitepaper could have conveyed. Our initial failure stemmed from treating AI deployment as a collection of independent projects rather than interconnected components requiring centralized coordination. The finance team's fraud detection system operated in complete isolation from the customer service chatbot, even though both analyzed the same customer interactio...