AI Framework Achieves 99%+ Accuracy in Mental Health Detection Using Brain Signals and Text

The framework targets millions affected by mental health disorders globally, potentially enabling earlier intervention and reducing diagnostic delays that currently burden patients and clinicians.

In laboratories and clinics around the world, millions of people wait weeks or months for a mental health diagnosis that may still rest on a clinician's subjective impression. A new AI system called MultiMindNet attempts to close that gap by reading two languages at once — the electrical rhythms of the brain and the words people use to describe their inner lives — achieving diagnostic accuracy above 99% for depression, anxiety, and stress. The work, published in PLOS, does not promise a cure for the systemic failures of mental healthcare, but it places a serious technological proof of concept

AI Framework Achieves 99%+ Accuracy in Mental Health Detection Using Brain Signals and Text

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