Asger Risom
A new system developed by Polish scientists could revolutionize mental health screening with fast, non-invasive AI diagnostics
As mental health disorders like depression and social anxiety become more widespread, scientists are racing to find faster and more accessible ways to detect them. A team of Polish researchers may have found a groundbreaking solution: using artificial intelligence to analyze eye movement patterns in just ten seconds.
Eye Tracking Meets Neural Networks
Researchers from three Polish universities have developed an AI-powered system that detects mental health issues by monitoring how people look at emotionally expressive faces.
During the test, participants observed images for ten seconds while eye-tracking sensors recorded their gaze patterns.
The system then used neural networks to analyze these “gaze paths” for signs of depression or anxiety.
“Eye movement patterns can provide objective data on our mental condition. In depressed people, we notice a tendency to focus on negative stimuli,”
explained Dr. Karol Chlasta of Kozminski University, one of the project’s co-authors.
People with social anxiety, he added, exhibit a phenomenon called hyperscanning—rapid facial scanning driven by heightened sensitivity to social cues.
The system currently achieves 60–70% diagnostic accuracy—comparable to traditional methods—making it a promising tool for initial screening, as reported by WP Tech.
Faster, Easier, and Scalable
What sets this method apart is its speed and ease of use. Unlike traditional psychological assessments, this approach is quick, non-invasive, and potentially integrable into everyday devices like laptops, smartphones, or VR headsets.
Dr. Chlasta likens it to wearable health tech that monitors sleep, except this system monitors vision to infer mental state.
The research team is also exploring voice analysis as an additional diagnostic tool.
In the case of many diseases, our voice changes slightly,
said Dr. Chlasta.
These subtle alterations, often undetectable to humans, can be flagged by AI to signal early-stage depression, dementia, or Alzheimer’s.
Challenges Ahead
While promising, the system isn’t yet ready for clinical deployment. Broader studies and more data are needed to confirm its reliability in real-world settings. Trust in AI diagnostics remains another barrier, requiring systemic changes and education before such tools can be widely adopted.
Still, the researchers are optimistic. In a world where depression may soon become the most diagnosed illness, ten seconds of eye contact might just be enough to change lives.