Artificial intelligence (AI) models for detecting depression and anxiety often report high accuracy in development data but rarely reach routine care, largely because of weak validation, uncalibrated outputs, opaque reasoning, unexamined bias and unclear clinical responsibility. This paper proposes a six-layer framework for AI-assisted screening of depression and anxiety that treats the model as decision support under professional oversight rather than as a diagnostic authority. The layers cover (1) ethical, consent-based multimodal data acquisition anchored on the PHQ-9, GAD-7 and a structured clinical interview; (2) quality control and preprocessing; (3) feature representation for questionnaire, clinical, language, speech and wearable data; (4) modelling with a logistic regression baseline and candidate support vector machine, random forest, gradient boosting and neural network models under nested cross-validation, calibration and external validation; (5) explanation and subgroup fairness auditing; and (6) a clinician-in-the-loop decision pathway with feedback for monitoring. A cross-cutting governance layer addresses privacy, security and accountability. A complete evaluation protocol is specified, including sample size planning, metric definitions, threshold selection and reporting under TRIPOD+AI. An analytic demonstration using published instrument accuracies shows why prevalence-aware interpretation is essential. At 5% prevalence, a screen with 0.88 sensitivity and 0.88 specificity yields a positive predictive value of only 27.8%, rising to 64.7% at 20% prevalence, while negative predictive value stays above 96%. The framework gives researchers and clinicians a reproducible template for developing, validating and deploying AI screening tools responsibly.

Mansi, Dr. Grace M. Basan Shrieh, "A Clinician-in-the-Loop, Explainable and Fairness-Aware Framework for AI-Assisted Screening of Depression and Anxiety: Design and Validation Protocol" International Journal of Scientific Research and Engineering Development, V9(5): Page(637-645) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.