Advancing Evidence-Based Psychiatry: A Platform for the Future of Mental Health Assessment

Evidence-Based Intelligent Decision Support

Automated symptom identification with direct mapping to diagnostic criteria
Multi-modal data integration combining AI interviews, clinical sessions, questionnaires, and emerging biomarkers
Real-time transcription and analysis of clinician-patient sessions
Transparent symptom-to-criteria mapping with complete audit trails for compliance
Decision support for professionals with evidence-based insights
Systematic documentation preserving clinical nuance while ensuring consistency

Automated Clinical Interactions

AI-powered voice agent conducting structured clinical interviews using validated frameworks (e.g. MINI, SIPS, MADRS)
Comprehensive patient questionnaire platform with integrated patient-reported outcome measures (PROMs)
Longitudinal patient monitoring through automated check-ins and symptom tracking enabling continuous quality improvement
Standardized assessment delivery ensuring consistency across diverse patient populations
Patient-friendly interface supporting engagement throughout the care journey

Our Vision for Psychiatry

March Towards Objectivity: By integrating autonomous standardized assessments and building on this with emerging biomarkers, we take leaps towards our goal of objectivity in psychiatry and reduction of subjective biases while preserving clinical judgment. Current studies include the MIND AIM study. Keep an eye out for our upcoming article in Translational Psychiatry.

Longitudinal Data Capture: Our platform tracks patient outcomes over time, generating the structured, long-term data essential for understanding symptom progression and treatment effectiveness. Examples include the PRIME study looking at relapse after first-episode psychosis.

Advancing Precision Psychiatry: Our multi-modal data integration and longitudinal tracking could enable identification of patient-specific treatment responses and biomarker patterns, supporting the development of personalized therapeutic approaches. We are actively investigating this, such as in TMS and esketamine response.

R&D: Objective Biomarker Integration

Advanced vocal biomarker analysis for detecting speech patterns associated with mental health conditions
Multi-modal data fusion combining traditional assessments with objective measures
Machine learning models trained on large-scale, well-characterized clinical datasets