As generative models produce increasingly convincing medical images, a natural question follows: do clinicians look at synthetic images the same way they look at real ones? In this ETRA 2025 study, we use eye tracking to examine how doctors’ gaze shifts between real and AI-generated medical images.

The context is that AI-generated imagery is entering medical workflows and research, yet we understand little about how experts perceive it. If a synthetic image subtly departs from real anatomy or texture, it may change how a clinician’s attention is deployed, even when the difference is hard to name explicitly. Gaze offers a window into that perceptual response.

Our study records and compares clinicians’ eye movements as they view real and generated images, looking for systematic differences in how attention is distributed. Such shifts would suggest that experts respond, consciously or not, to cues that separate synthetic from authentic medical content.

This is relevant both to trust in AI-generated imagery within clinical settings and to the possibility of using gaze as a signal for detecting synthetic content. This was joint work with D.C. Wong, B. Wang, G. Durak, M. Tliba, A. Chetouani, A.E. Cetin, and colleagues.

Presented at ETRA 2025; see my Publications page.

See the paper for the full methodology and results.