AI-Driven Precision: Enhancing Mental Health Diagnostics Through AI-Assisted Interviews

Alba AI conducted conversational assessments in 303 participants and showed higher concordance with confirmed psychiatric diagnoses than conventional rating scales, improving early diagnostic accuracy. Clinically, AI-assisted interviews improved alignment with confirmed diagnoses versus standard rating scales.

Unlike brief symptom checklists that score isolated items, the system used structured, open-ended questions mapped to DSM-5 criteria and captured contextual nuance across domains. In a randomized comparison against standard rating scales (n=303, diagnostic endpoints based on confirmed diagnoses), the AI approach showed superior differentiation of overlapping presentations. That finding suggests the tool can enhance initial diagnostic triage while not replacing clinician judgment.

The clearest diagnostic gains were in distinguishing depression from anxiety and across seven of nine diagnostic categories where overlapping symptoms commonly confound standard scales; discrimination metrics favored the AI approach. Participants described the interaction as empathic and supportive, which correlated with greater engagement and likely more reliable symptom disclosure. Improved accuracy together with perceived empathy strengthens the quality of early assessment and case identification.

AI outputs can be delivered as structured differentials, DSM-5 checklists, or flagged workflow items annotated with confidence scores; output from AI-assisted interviews can be routed into electronic records to preserve clinical oversight. Use these augmenting outputs to inform evaluation and treatment planning while retaining mandatory human sign-off on final diagnoses.

Primary ethical risks include data privacy and consent, algorithmic bias, limited explainability, and over-reliance on automated outputs—each requires targeted mitigation. Robust consent processes and data governance protect patient information. Diverse training data and periodic algorithmic auditing reduce bias and promote equitable performance. Concise clinician-facing rationales and explainability modules improve transparency for decision-making. Mandatory human review and diagnostic sign-off prevent over-reliance on automated labels.

These safeguards are essential to realize clinical benefit safely.

Key Takeaways:

• Alba AI demonstrates improved differentiation of depression vs anxiety, improving triage accuracy for outpatient clinics.
• Primary care and mental health intake teams, gaining richer previsit assessments that increase diagnostic clarity.
• Integration and real-world evaluation with audit and bias monitoring to validate impact and guide safe implementation.