Every healthcare software vendor now offers “AI-powered” features. But for organizations evaluating these tools, critical questions remain unanswered. When does AI software require medical device regulation? How do you control something inherently unpredictable? And how do you tell genuine safety assurance from marketing?

The core technical challenge with large language models is their probabilistic nature. They don't behave the same way twice. Making them safe for clinical use requires deliberate engineering: design constraints, output guardrails, structured validation, and rigorous clinical evaluation. But safety doesn't end at deployment. The session covers why continuous observability infrastructure is essential for AI operating in unpredictable clinical environments, and how responsible manufacturers handle model updates with full traceability rather than silent changes.

Drawing on experience from building regulated AI clinical software, including medical scribes, the presentation shares practical examples of how these principles work in production. Attendees will leave with the questions that matter when evaluating any AI vendor: Is this a registered medical device or just marketed as “compliant”? How are outputs validated? What happens when the underlying model changes? How is performance monitored after deployment? Where is data processed and who can access it?

Healthcare organizations face real pressure to adopt AI while managing patient safety and institutional risk. Vendor marketing often obscures meaningful differences in accountability. This session gives procurement and clinical informatics teams the knowledge to evaluate AI tools critically and to push the market towards genuine safety standards.