
Smart rings and LLMs predict depression symptoms
Giving consumer smart ring data to a raw language model to spot depression fails completely, unless you teach it how to think first.
Discover the newest research about AI innovations in 🧠Mental Health.

Giving consumer smart ring data to a raw language model to spot depression fails completely, unless you teach it how to think first.

An algorithm can spot patients at risk of suicide within 30 days using only basic emergency room intake data.

A digital twin model proves that mental health is not just a quality-of-life issue but a direct driver of chronic physical disease.

Standard medical billing codes are failing to track patient crises, leaving healthcare systems blind to critical mental health risks.

A new federal payment model is forcing digital health companies to prove their tools actually heal patients, not just log clinical hours.

New research reveals that as digital therapy sessions drag on, artificial intelligence loses its ability to detect suicidal ideation, even as human clinicians remain perfectly sharp.

Electronic health records are failing vulnerable patients because standard billing codes ignore the messy reality of clinical notes.

Doctors write down clues about their patients’ loneliness, but those warnings usually sit buried in unstructured text where no one can find them.

When vulnerable teenagers turn to artificial intelligence for mental health advice, they are not finding a cure—they are finding a mirror that tells them what they want to hear.

The regulatory approval of a non-invasive brain stimulation headset for PTSD exposes a massive, decades-long stagnation in psychiatric drug development.