Depression
Depression screening by GIA®, powered by digitalhumanOS™, uses Voice AI, Computer Vision, and Speech Biomarkers to detect depression through a single patient conversation. Screening performance: 81.6% accuracy. Screening takes under 5 minutes with results in 60 seconds.
Untreated depression steals joy and prolongs recovery, impacting lives and families.
Depression significantly hinders rehabilitation progress. Early detection allows for timely intervention, improving patient well-being and reducing readmission rates in post-acute care.
Key Facts
- Screening Time
- Under 5 minutes
- Results
- 60 seconds
- Modalities
- Voice + Vision + Speech
- Registration
- 510(k)
- Status
- Live
About Depression screening.
How does this system identify depression?
Our AI analyzes voice, speech, and facial cues indicative of depression, providing a risk assessment.
What level of accuracy can I expect?
Our depression screening model achieves 81.6% accuracy, helping you prioritize patients for further evaluation.
How does early detection of depression impact my organization's bottom line?
By improving patient outcomes and reducing readmissions, early detection positively impacts financial performance and resource allocation.
The science behind Depression screening.
A January 2026 clinical white paper demonstrates that machine learning models trained on spontaneous speech can serve as an effective first step in identifying individuals at risk for depression and anxiety — enabling earlier intervention at scale.
Behavioral Health Assessment Using Vocal Biomarkers (2026-01)Peer-reviewed research validates speech-based depression severity detection — enabling nuanced assessment beyond binary present/absent screening.
Depression Severity Detection Using Read Speech With A Divide-And-Conquer Approach (2022-03)Clinical research demonstrates reliable audio-based detection of both anxiety and depression through vocal biomarker analysis — validating voice as a dual-condition screening modality.
Audio-based Detection of Anxiety and Depression via Vocal Biomarkers (2023-09)Research confirms that anxiety and depression are detectable from standard telephone conversations — without specialized equipment, clinical settings, or patient prompting.
Detecting Anxiety and Depression from Phone Conversations Using X-vectors (2022-08)A systematic scoping review from the University of Málaga, published in Biology (MDPI), confirms that voice production consistently engages the autonomic nervous system — with vocal markers providing measurable signals of stress, cognitive load, emotional state, and subclinical clinical conditions detectable before symptoms appear.
Mapping the Neurophysiological Link Between Voice and Autonomic Function: A Scoping Review — Biology, MDPI (2025-10-10) · University of Málaga, Biomedical Research Institute of Málaga (IBIMA Platform BIONAND)DOI: 10.3390/biology14101382 →