The transition of generative AI from theoretical models to production grade clinical tools requires a shift from standard performance metrics to adversarial stress testing. As healthcare institutions integrate Large Language Models (LLMs) into decision support and patient interaction, the risk of safety failures, hallucinations, and model degradation under pressure becomes a critical liability. This session explores a systematic approach to clinical red teaming, moving beyond generic jailbreaking to context specific adversarial mechanics. By leveraging insights from a comprehensive scoping review of clinical AI safety, I will present a methodology for stress testing models using noisy, high dimensional patient data.

The presentation focuses on identifying specific failure points where LLMs encounter real world clinical noise, such as inconsistent electronic health record documentation and complex multimodal imaging inputs. We will discuss the development of a systematic analysis pipeline designed to visualize the relationships between AI architecture and adversarial vulnerabilities. This framework goes beyond simple accuracy to evaluate clinical humility and intersectional equity, ensuring that models remain robust across diverse patient populations. Attendees will gain a technical roadmap for implementing red teaming protocols that prioritize clinical safety and institutional resilience in the face of unpredictable generative behaviors.