Research track
Voice Biomarker
Studies acoustic patterns such as pace, pauses and prosody, as described in our clinical research paper.
Research track. Not a diagnostic tool.
Meet this agentClinical research paper
Multi-modal
behavioral health intelligence.
A clinical framework reporting 91.3% diagnostic accuracy through multi-modal integration, evaluated across 2,696 patients in an internal validation study.
Medera Research TeamPublished September 202515 min read
A note on figures. The accuracy, AUC, sample-size, time-reduction and comparative figures presented throughout this paper are internal, illustrative results from Medera’s own validation work. They have not been independently audited or peer-reviewed, and should not be read as claims of regulatory clearance or independent verification. The multi-modal assessment described here is a research track; in the product, Medera is a clinical support tool, not a diagnostic instrument.
Abstract
Abstract
Behavioral health disorders affect over 970 million people globally, with significant treatment gaps due to limited access to specialized care. This research presents Medera, a system designed to augment clinical assessment capabilities rather than replace them.
Medera integrates four modalities through transformer architectures: visual (facial expressions), acoustic (voice patterns), linguistic (speech content) and physiological (vital signs) data streams. The system achieved 91.3% overall diagnostic accuracy (95% CI 89.7–92.9) with an AUC of 0.93, demonstrating calibration (Brier score 0.082) and consistent performance across demographic subgroups.
Assessment time was reduced by 42% while maintaining the interpretability and safety standards required for clinical deployment across diverse healthcare settings.
Methodology
Four streams,
one clinical picture.
The assessment framework captures 290 distinct clinical data points across multiple domains, drawing on 157,000+ longitudinal health assessments and 275+ multi-modal clinical interviews.
1
Visual
Facial expression and gaze
- ViT-B/16 backbone
- 68 facial landmarks
- Action units (FACS)
- Gaze tracking
2
Acoustic
Voice and prosody
- WavLM-Large
- Pitch variation
- Speech rate
- Pause patterns
3
Linguistic
Speech content
- BioClinicalBERT
- Sentiment analysis
- Topic modeling
- Syntax complexity
4
Physiological
Autonomic and activity signals
- Heart rate variability
- Skin conductance
- Sleep patterns
- Activity levels
Results
Diagnostic performance,
in internal validation.
Overall accuracy91.3%95% CI 89.7–92.9
Sensitivity88.7%95% CI 85.2–91.6
Specificity93.1%95% CI 91.0–94.8
PPV84.3%95% CI 80.1–87.8
NPV94.9%95% CI 93.2–96.3
F1 score0.86595% CI 0.842–0.887
95% confidence intervals calculated using bootstrap resampling (n = 10,000). Optimal threshold 0.62 · Youden’s index 0.818 · DeLong test p < 0.001 vs baseline · Brier score 0.082.
Accuracy by condition
- Depression
- 92.1%
- Anxiety
- 89.8%
- PTSD
- 87.3%
- Bipolar
- 85.6%
Validation
Three-stage validation.
n = 189
Baseline validation
Proprietary clinical dataset with a 70/15/15 split for comprehensive baseline validation.
n = 2,007
Real-world encounters
Real-world encounters with parallel clinician assessment.
n = 500
Comparative study
Internal validation study comparing Medera to standard care.
Evaluations used an identical test dataset (n = 2,696) with a consistent demographic distribution, standardized DSM-5-TR criteria, blinded review by board-certified psychiatrists, and inter-rater reliability κ > 0.85.
Safety
Safety monitoring.
- Human clinician review required for all diagnoses
- Automatic escalation for crisis indicators
- Confidence threshold monitoring
- Continuous bias detection
- Real-time performance monitoring and weekly calibration checks
- Monthly fairness audits and a quarterly clinical review board
Limitations
What it cannot do.
- Requires high-quality input data for optimal performance
- Not validated for acute crisis intervention scenarios
- Potential for automation bias requires ongoing clinician education
- Long-term outcome data collection ongoing (5-year follow-up planned)
Medera augments clinical expertise; it does not replace it. A licensed clinician reviews and signs every care-impacting output before it reaches a patient.
Conclusions
Conclusions
This research establishes a paradigm for AI in behavioral health where technology serves as a force multiplier for clinical expertise rather than a replacement. The system’s ability to maintain accuracy while ensuring fairness across diverse populations addresses critical gaps in mental healthcare accessibility.
In internal reporting, clinicians using Medera described improved diagnostic confidence (89%), reduced administrative burden (67%) and an enhanced ability to focus on therapeutic relationships (94%).
The research track
The agents that carry
this research.
Voice, physiological and neuro biomarker agents study the signals this paper describes, alongside clinicians and never in place of them.
Research track
Physiological Biomarker
Studies vital-sign signals alongside the conversation, as described in our clinical research paper.
Research track. Not a diagnostic tool.
Meet this agentResearch track
Neuro Biomarker
Explores how neurological signals could add context to assessment, in research settings only.
Research track. Not a diagnostic tool.
Meet this agentAgents marked Research track are part of Medera's research program, described in our clinical research paper. They are not diagnostic tools.
