Most LLM-in-healthcare talks come from one side or the other: either the clinical side, which tends to stay conceptual, or the engineering side, which tends to treat "clinical context" as a footnote rather than a design constraint. I sit across both — as a registered nurse and clinical advisor with extensive hands-on experience, and as a product developer who now builds and architects these systems directly. That combination is rare, and as a woman working in a space that's still heavily engineering-dominated, it's a perspective I don't think gets enough airtime either. It's exactly what lets a talk like this go deep on the technical side without losing what actually makes these systems safe or dangerous in a real ward.
Martina Wyss is an LLM and Healthcare AI Specialist working at the intersection of large language models, medicine, and digital health. With hands-on expertise in training, evaluating, and fine-tuning LLMs—specializing in prompt engineering and Reinforcement Learning from Human Feedback (RLHF)—she focuses on building safe, clinical-grade AI applications. Combining her technical LLM focus with a background as a registered nurse (Dipl. Pflegefachfrau HF) and a degree in human-centred design from the University of the Arts London, Martina bridges the gap between AI engineers, clinical workflows, and patient needs to ensure healthcare LLMs are accurate, reliable, and practical for real-world deployment.
