Situation​

•  Psychiatric diagnosis still relies heavily on subjective interviews and patient self‑reports.​

•  Subtle emotional cues in facial micro‑expressions often go unnoticed by clinicians.​

•  Differentiating overlapping psychiatric conditions (e.g., depression vs. anxiety) is challenging without objective markers.​

Impact​

• Subjectivity leads to delayed or inaccurate diagnosis.​

•  Clinicians struggle to track patient progress objectively across sessions.​

•  Lack of quantifiable emotional data reduces the ability to personalize treatment plans and measure outcomes effectively.​

Solution​

•  Emotiview AI platform applies facial expression recognition and emotion analysis during psychiatric consultations.​

•  Uses advanced computer vision models to detect micro‑expressions, affective states, and nonverbal cues in real time.​

•  Provides continuous, objective emotional insights that complement clinician observations.​

•  Flags emotional trends and anomalies to support differential diagnosis.​

•  Enables personalized treatment planning by tracking emotional response patterns over time.​

Benefits​

• Improved diagnostic accuracy by adding objective emotion markers to traditional assessments.​

•  Early detection of mood disorders through subtle facial cues.​

•  Better patient monitoring with quantifiable emotional progress reports.​

•  Enhanced personalization of therapy, leading to improved patient outcomes.​