David Stutz

Having worked on AI for health in the past few years, contributing to Med-Gemini and AMIE, I have witnessed a quite significant shift in the type of research that is required.

Introduction Throughout my academic career, I worked on various problems surrounding the robustness of deep learning models.

In September 2025, I had the opportunity to attend the RAISE symposium on responsible and ethical AI for healthcare.

Introduction Efficienctly drafting useful reviews and properly writing rebuttals can be incredibly difficult and needs quite a bit of practice.

Over the past two years, I've been asked repeatedly at conferences and talks about my general opinion on watermarking AI-generated content.

Planning and conducting rater studies has become a core activity in AI research as more and more tasks become very ambiguous, depend on subjective opinions or require specialized expertise.

Handling rater disagreement is becoming more important, especially for evaluating large language models (LLMs) but also in domains such as health.

A core component of research is the communication of research ideas.

When working in AI, especially when doing an empirical PhD, the engineering required for effective experimentation is incredibly important.

Are code and data available?