A Safety-Gated Multimodal AI Backend for Mental-Health Support: Hierarchical State Representation, Conservative Risk Fusion, and Controlled Generation in Anian
Anian is presented, a safety-gated multimodal AI backend for perinatal mental-health support and mindfulness-intervention routing that supports the internal feasibility of the label framework and gating logic but does not establish clinical validity, diagnostic accuracy, real-world safety, or effectiveness.
Abstract
Safety-critical mental-health support systems must distinguish when supportive conversation is appropriate from when free-form generation should be blocked. This paper presents Anian, a safety-gated multimodal AI backend for perinatal mental-health support and mindfulness-intervention routing. Anian is not intended to diagnose psychiatric conditions or replace clinical care or crisis intervention. Its modular pipeline places generative AI downstream of structured state representation, conservative risk fusion, and response gating. User text or voice-derived ASR transcripts are mapped into four linked layers: L1 emotion states, L2 psychosocial constructs, L3 safety risk, and L4 intervention routes. Local text- and rule-based safety evidence is fused with external voice-derived evidence using a highest-risk-priority rule, S_fusion = max(S_local, S_external). At moderate or high fused risk, ordinary AI-generated responses and text-to-speech delivery are blocked and replaced by fixed safety content and prompts for human support. An internal prototype evaluation used approximately 858,295 normalized records from public emotion, dialogue, mental-health-related, and Chinese dialogue corpora within a weak-label and rule-derived framework. Micro-F1 scores were 0.9604 for L1 emotion classification, 0.9144 for L2 psychosocial constructs, and 0.9742 for L4 routing. In a controlled safety stress test of 233 samples, the L3 rule engine achieved high-risk recall of 1.0000 within predefined scenarios. These findings support the internal feasibility of the label framework and gating logic but do not establish clinical validity, diagnostic accuracy, real-world safety, or effectiveness. We report the architecture, ontology, safety-fusion mechanism, prototype evaluation, error-analysis plan, and roadmap for expert-reviewed and real-world validation.
A model-agnostic safety governance architecture that combines contextual risk detection, reasoning-based verification, and protocol-guided response generation for multi-turn mental health interactions is developed, providing a scalable framework for safer deployment across models.
A. Areias, Catarina Botelho, António Farinhas et al.· 0 citations
Findings support RAG as a promising approach to improve the accuracy, consistency and safety of LLM-driven DMHIs through a controlled comparison of RAG-enabled versus RAG-disabled modes.
A. Gupta, Akshat Surolia, Shubham Mishra et al.· 0 citations
Multimodal behavioral analysis offers a scalable approach to assessing depression, anxiety, and stress, yet generic fusion models often ignore the psychometric structure of questionnaire labels. In DASS-21, risk labels are derived from ordered symptom items through fixed item-to-subscale mappings. We propose \textbf{DynaBridge}, a dynamic summary-guided cross-task multimodal framework for DASS-structured mental health assessment. DynaBridge encodes acoustic, visual, and textual cues across multiple sessions and augments them with frozen-LLM-generated DASS-aware summaries as participant-level semantic evidence. It predicts ordinal item distributions, reconstructs depression, anxiety, and stress risk evidence from item-level soft scores, and fuses this evidence with direct multimodal risk predictions. A confidence-aware refinement strategy further incorporates high-confidence semantic cues conservatively. On the official AdoDAS validation split, DynaBridge outperforms the official baseline and representative multimodal methods, achieving 0.5012 mean F1 for D/A/S risk prediction and 0.3216 mean QWK for DASS-21 item prediction. These results show the value of bridging multimodal cues, semantic summaries, and DASS-21 psychometric structure.
Shiyu Teng, H. Yu, Jiaqing Liu et al.· 0 citations
The rapid growth of tele-counseling and the use of lay counselors in high-volume, low-resource mental health services has created a need for scalable tools for early detection and triage. Effective personalization now requires stratifying individuals by dominant symptom profiles, such as appetite, agency, anxiety, and sleep disturbances. Depression symptoms vary widely, even among those with similar scores, reflecting distinct psychophysiological and cognitive-affective patterns. In tele-mental-health settings, where contextual cues are limited, multimodal behavioral signals from natural interactions can complement traditional assessments. Using synchronized audio, video, and text data from the EDAIC dataset (N=275), we propose a multimodal learning framework to classify five clinically validated outcomes: Depression, Appetite disturbance, Agency impairment, Anxiety, and Sleep problems. We developed a comprehensive multimodal machine-learning pipeline, incorporating automated dataset construction, modality-specific feature extraction (acoustic, facial action unit, linguistic), and supervised learning with cross-validation. Labels were derived from validated scoring rules to ensure clinical relevance. Sentiment analysis revealed lower sentiment scores in participants with high Depression, Anxiety, or Agency scores, but no significant differences in Appetite or Sleep severity. Model performance was assessed across three scenarios: text (transcripts), phone calls (audio + transcript), and video calls (audio + video + transcript). Temporal models (CNN+BiLSTM) achieved over 65% accuracy across modalities, while a fine-tuned temporal model for depression detection using video calls reached an accuracy of 81% with an f1-score of 0.79, demonstrating that our approach performs on par with state-of-the-art methods while maintaining a substantially lower computational footprint suitable for edge deployment. SHAPley analysis identified key audio and video features for detecting Depression and other symptoms. Beyond depression screening alone, our framework is among the first to simultaneously stratify individuals across five clinically relevant symptom domains as per Research Domain Criteria (R-DOC) enabling more personalized and targeted intervention. A translational avatar-based interface validated system operability, demonstrating the potential for scalable, objective mental-health assessment in tele-counseling.
Amal Jude Ashwin Francis, Ahmad Raza, Nischay Patel et al.· Bio Systems· 0 citations
BACKGROUND
Natural language processing and large language model systems are increasingly used to support mental health documentation, screening, and follow-up planning. In counseling contexts, model outputs may influence diagnostic framing, risk recognition, and clinical record content. Static performance metrics and fluent generated summaries are not sufficient to support safe implementation without governance, safety gating, human review, and monitoring.
OBJECTIVE
This study aimed to develop a human-governed clinical informatics framework for safe AI-assisted mental health counseling and make the formative evidence base and requirement-mapping process traceable.
METHODS
We conducted a secondary framework development and requirement mapping study using the Korean AI Hub psychological counseling dataset, official data description and use documents, released KLUE-BERT risk prediction model materials, released KoAlpaca summary generation resources, and a deidentified 139-case rule-based summary safety screening audit table derived from the original summary comparison file. Raw counseling transcript text, reference summary full text, and generated summary full text are not included in the manuscript or supplementary materials. We extracted failure modes from documented data and model characteristics, released code and configuration files, documentation-reported model metrics, and rule-based proxy flags. Each failure mode was mapped to safety controls, operational criteria, and deployment-level requirements.
RESULTS
The official documents described 1661 counseling sessions and 465,474 paragraph-level tokens across depression, anxiety disorder, addiction, and normal control groups. Of the 1661 sessions, the documented split included 1339 (80.6%) training, 173 (10.4%) validation, and 149 (9%) test sessions. The summary generation materials documented 1278 training summaries and 139 test summaries. Documentation-reported model metrics included KLUE-BERT accuracies of 71.43% for depression, 73.53% for anxiety, and 66.67% for addiction and KoAlpaca BERTScore precision, recall, and F1-score values of 62.13%, 59.56%, and 60.80%, respectively. The 139-case screening table contained 77 (55.4%) depression, 31 (22.3%) anxiety, and 31 (22.3%) addiction cases. Rule trigger rates included unsupported content proxy flags in 41% (57/139) of cases, overdiagnostic expression proxy flags in 31.7% (44/139) of cases, medicalized expression proxy flags in 54.7% (76/139) of cases, and any rule-based proxy flag in 91.4% (127/139) of cases. These values are conservative rule trigger rates rather than confirmed clinical error rates. The findings informed a 7-stage workflow, 6 safety control layers, an operational safety gate, a workflow-to-control crosswalk, deployment-level transition criteria, and a constructed high-risk example.
CONCLUSIONS
AI-assisted mental health counseling should be implemented as a governed clinical information workflow rather than as an autonomous diagnostic or documentation pathway. The proposed framework specifies safeguards and validation requirements for future supervised evaluations, but it does not itself establish clinical safety or clinical effectiveness. Prospective simulation, clinician usability testing, patient or client feedback, and independent expert validation remain necessary before routine deployment.
The rapid integration of artificial intelligence into clinical voice research has created promising opportunities for the early identification of mental health concerns, particularly among individuals with communication impairments. In their recent study, Wang and Sambamoorthi (2026) developed an interpretable and fairness-aware Random Forest model that predicts mental health disorders using acoustic features derived from the Bridge2AI-Voice dataset. By organizing these features into clinician-friendly domains of voice stability, speech prosody, and voice clarity, the authors achieved solid predictive performance, with area under the receiver operating characteristic curve values of approximately 0.83 to 0.84. The model revealed etiology-specific acoustic patterns and employed SHapley Additive Explanations and partial dependence plots to enhance transparency. Furthermore, the demonstration of strong gender fairness, including perfect counterfactual prediction invariance, represents a commendable step toward responsible artificial intelligence in voice science. These contributions position the study as a significant advancement in the development of ethical and clinically relevant voice-based screening tools.