Researchers explore AI and digital tools to improve mental health diagnosis and care

Researchers are developing new methods to improve the detection and management of mental health conditions. At the University of Southern California, the PRECOG project is investigating whether artificial intelligence can identify biological markers for depression and suicide risk. By analyzing involuntary signals such as brain electrical activity via EEG, eye movements, and changes in perspiration, the team aims to find measurable patterns that distinguish healthy individuals from those experiencing suicidal thoughts.

In a separate effort to improve psychiatric care consistency, researchers at Carilion Clinic have evaluated a digital training program called METRIC. The program is designed to help clinicians implement measurement-based care (MBC) by using standardized patient-reported outcome measures (PROMs). The study found that the online platform, which utilizes educational videos and simulations, helps clinicians more effectively incorporate symptom questionnaires into routine outpatient psychiatric practice.