Mental health providers face unprecedented labor shortages, with many agencies struggling to fill positions (1). According to new data released by the World Health Organization (WHO), more than 1 billion people are living with mental health disorders, with conditions such as anxiety and depression inflicting immense human and economic tolls. The global median number of mental health workers stands at 13 per 100,000 people, with extreme shortages in low- and middle-income countries (2). In the United States alone, nearly 96 unmet needs for prescribers, indicating a critical shortage of available professionals whereas in Mexico, only 1.1 psychiatrists per 100,000 population exist in the public sector, exacerbating the lack of adequate care for mental health disorders (3). This global treatment gap underscores that the shortage of mental health professionals is not limited to North America but represents a critical international public health concern. The shortage of mental healthcare providers significantly hampers the quality and accessibility of mental health services. This crisis is characterized by high vacancy rates, increased turnover, and an inadequate distribution of professionals, leading to the delayed or cancelation of patient appointments and even the closure of wards in various regions (1). A severe shortage of mental health professionals, coupled with barriers like stigma, resource constraints, and geographical limitations, creates a considerable gap in the provision of effective support.

With the rise of AI, new opportunities have emerged to transform mental healthcare, offering innovative approaches that can bridge this gap (4). AI-powered chatbots and virtual assistants represent a significant breakthrough, providing accessible, scalable, and cost-effective alternatives to traditional mental health support. It is important to distinguish, however, between AI chatbots that provide conversational support and AI-based diagnostic tools that analyze clinical data for symptom detection and prediction. While chatbots primarily facilitate self-help and engagement, diagnostic systems aim to assist clinicians in making data-driven assessments, requiring higher levels of accuracy and clinical validation. These AI tools utilize natural language processing (NLP) and machine learning (ML) to simulate therapeutic conversations, helping users engage in a more personalized and supportive experience. AI-powered diagnostic systems, on the other hand, analyze multimodal clinical data—such as patient narratives, behavioral markers, and physiological parameters—to identify symptom patterns and support clinical decision-making. AI-powered mental health diagnostics systems offer promising solutions to improve mental health diagnostics, as these systems can assist by analyzing vast amounts of data to improve predictions by differentiating between similar symptoms, aiding therapists in accurate diagnoses, and improving patient treatment outcomes (5). This capability facilitates the early detection of mental health conditions and enables timely interventions, ultimately leading to improved patient outcomes while enhancing monitoring capabilities (6).

However, the use of AI in mental health diagnostics is not without its challenges. Major challenges include clinicians’ trust, ethical concerns, and the opaque “black box” nature of AI. Explainable artificial intelligence (X-AI) addresses these issues to a certain extent by demystifying AI decision-making, allowing clinicians to understand the reasoning behind system-generated diagnoses, which helps foster trust in the technology (4). By focusing on X-AI-driven transparency, such system can not only build trust but also make mental health resources more accessible to underserved populations by providing 24/7 support and overcoming geographical barriers and stigma associated with mental health treatments (7).

Furthermore,it is noted that the validation of AI models is essential to ensure their performance aligns with real-world clinical environments, as accuracy metrics may not fully capture their effectiveness (8). The lack of standardized clinical attribution across various diagnostic models makes it more challenging to integrate with existing healthcare diagnostic systems. This research presents a novel, systematic, clinically relevant approach that unifies the power of ML with human-interpretable, AI-generated explanations and reasoning capabilities, clinically grounded through Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT) attribution. SNOMED CT provides a globally recognized, standardized vocabulary that enhances interpretability and ensures interoperability across healthcare systems, thereby facilitating the seamless exchange of clinically meaningful data. Integrating SNOMED CT (9) for structured, evidence-based attribution can further enhance diagnostic accuracy and contextual relevance. By adding a layer of clinically relevant attribution to the existing X-AI layer, the system could improve the clinical reliability of AI-assisted mental health diagnostic interventions and encourage broader acceptance within existing healthcare diagnostic systems.

The integration of AI within healthcare systems has been widely advocated for its potential to enhance personalization, contextual adaptation, and scalability in mental health interventions (10). AI-powered chatbots and automated systems can reduce clinician workload, streamline routine processes, and extend access to mental healthcare without incurring proportional increases in cost. These benefits have positioned AI as a promising tool for broad-reaching support in mental health services. However, these advantages must be balanced against ongoing challenges including data privacy concerns, algorithmic bias, a lack of transparency, and the need for rigorous clinical validation (7, 11).

To address transparency and trust concerns, X-AI techniques such as Local Interpretable Model-Agnostic Explanations (LIME)—which approximates a complex model locally to explain individual predictions—and SHapley Additive exPlanations (SHAP)—which assigns each feature an importance value based on cooperative game theory—have been introduced (12). These tools help clinicians understand the features driving AI predictions, thereby enhancing diagnostic precision and improving resource allocation (13). Their adoption is essential in mitigating risks associated with opaque decision-making and potential misdiagnosis, ultimately supporting patient welfare (14). Techniques such as LIME and SHAP, as highlighted in recent studies, highlight the strengths (e.g., SHAP’s consistency) and limitations (e.g., LIME’s instability), emphasizing the need for careful selection of explanation methods depending on the clinical context. As AI systems become increasingly pervasive in mental healthcare, ethical and regulatory considerations gain critical importance (Algorithm). Clinical validation ensures that AI-driven diagnostics meet standards of transparency, safety, and accuracy, while proper clinical attribution enhances trust between clinicians and patients (11). Attribution methods—used to assess feature importance and understand model decisions—are crucial for mitigating algorithmic bias and ensuring equitable care for diverse populations (15). Clear attribution frameworks also strengthen the ethical foundation of AI-based mental health diagnostics by improving transparency and accountability (16, 17). SNOMED CT plays an increasingly central role in advancing the reliability, interoperability, and clinical relevance of AI systems. As a comprehensive, standardized clinical terminology, SNOMED CT enhances the alignment of AI-generated insights with globally recognized medical standards, improving both accuracy and trust (18). The linkage between SNOMED CT and model outputs can occur through both post hoc mapping, where predicted diagnostic terms are translated into SNOMED concepts, and integration during model training, where structured terminology is embedded directly into the model to guide representation learning and terminology-aware predictions. This dual integration pathway enhances the interpretability and utility of explanation tools such as LIME and SHAP. Recent empirical studies demonstrate that embedding SNOMED CT into AI pipelines improves fairness by reducing terminology-related bias and enhances diagnostic consistency across healthcare settings (19). Case studies have shown that SNOMED CT-driven models yield improved clinical decision support outcomes, particularly in mental health contexts where linguistic ambiguity often complicates diagnosis. Furthermore, the structured representation of patient symptoms supports personalized and context-specific treatment planning while enabling scalable, interoperable, and clinically meaningful AI applications. Thus, SNOMED CT-based clinical attribution provides a robust foundation for trustworthy X-AI, addressing key challenges related to transparency, fairness, and ethical compliance in mental health diagnostics.

The MATRIX framework is depicted in Figure 3. The algorithm underlying this tool leverages NLP and ML techniques to detect mental and health symptoms based on conversational responses with the respondent in real time. The algorithmic steps and specifications of various data resources are detailed in the following sections. User responses are parsed in real time through an NLP pipeline that identifies significant linguistic markers, extracting key features such as emotional cue indicators and patterns, which are then analyzed using the PHQ-9 SYMPTOM ONTOLOGY dataset (detailed in the next section) to enhance the precision of PHQ-9 questionnaire classification.

Users’ current state of mind shared in a natural language format, parsed through an NLP pipeline reaches the next stage where it is further analyzed—guided by questions rooted in the standarized PHQ-9 questionnaire, screening depressive symptoms and assessing symptom severity(as per major categories described in the previous section) based on the input received from the NLP pipeline.

This severity score guides the rule-based internal system in generating the most likely diagnosis. To achieve an accurate diagnosis based on symptom severity scores, each depressive symptom feature is weighted and assessed by a five-layered (obtained through hyperparameter testing) neural network model trained on a vast dataset of the PRIMATE ontology, which contains widely annotated datasets. This system-generated diagnosis is then fed to a reasoning layer, which draws on clinical guidelines to understand why specific symptoms have been flagged and generates explanations with the use of language model, BERT model in this case, to ensure privacy of users’ data (more details are provided in the Results section), enhancing the system’s interpretability and helping clinicians understand better the rationale behind specific diagnosis.

Further refining its diagnostic utility, our system attributes detected symptoms and diagnoses by fetching relevant SNOMED CT IDs in real time, which are recognized clinical codes used in EHRs worldwide. By dynamically associating symptoms with SNOMED CT identifiers, the algorithm ensures that each AI-based classification and diagnosis aligns with clinically accepted mental health diagnoses, providing a direct pathway from initial symptom detection to actionable diagnostic insights.

The MATRIX model (flowchart depicted in Figure 4) dynamically evaluates user input in real time, mapping it onto a spectrum of symptom severity, generating the most likely diagnosis, and embedding a layer of reasoning that offers transparent explanations for the system’s decision and attributing the diagnosis with a specific clinically relevant SNOMED concept identifier. Mental health practitioners can thus access these AI-generated recommendations, along with clinically relevant justifications, all in real time, enabling them to review, adjust, or supplement the AI suggestions seamlessly.

MATRIX, as a comprehensive unified interface, sets a new standard in digital mental health support by enabling rapid assessments and AI-driven responses. By streamlining the initial evaluation process, it reduces consultation time, allowing mental health professionals to concentrate on critical interventions. In addition, this approach significantly enhances accessibility, extending support to underserved populations through digital platforms. It empowers practitioners with reliable, data-driven insights for well-informed decision-making. By automating diagnostic documentation and structuring responses for a quick review, the system optimizes the efficiency of mental healthcare services, improving patient throughput and saving valuable clinician time.

The system integrates components for data preprocessing, feature extraction using transformers, and classification using a custom-built neural network. The system accepts input from a JSON file containing textual posts (post_text) for initial system training and the PHQ-9 symptom ontology file, with columns representing PHQ-9 questions and their associated symptoms. The data preprocessing pipeline iterates through the posts, identifying relevant symptoms from the ontology, and associates them with corresponding PHQ-9 questions. Feature extraction and embedding generation: Text embeddings are generated using the Sentence Transformers (all-MiniLM-L6-v2) for semantic representation. Input text is tokenized and padded to 128 tokens, and mean pooling is applied over token embeddings to produce a fixed-size vector representation.

Further, a feedforward neural network is employed for multiclass classification. The architecture comprises an input layer that matches the dimensionality of the embedding vectors, a fully connected hidden layer with 128 neurons and ReLU activation, and an output layer that provides a probability distribution across PHQ-9 questions using the Softmax activation function. For classification decisions, a confidence threshold of 0.55 is applied. Predictions with probabilities below this threshold are suppressed to reduce false positives; otherwise, raw probabilities contribute to downstream reasoning. The network is optimized using the Adam optimizer with a learning rate of 0.001 (obtained through hyperparameter testing), and the loss is calculated using the CrossEntropyLoss function. The model is trained for 32 epochs, optimizing weights through backpropagation. The evaluation is performed by comparing predicted classifications with true labels for the held-out test set to assess performance. Moreover, to generate a diagnosis, the system counts the occurrences of predicted PHQ-9 questions and identifies patterns that indicate major depressive symptoms. The diagnostic results summarize the predominant questions and their frequency, providing insights into symptom distribution. The model generates predictions in the form of a system-generated PHQ-9 checklist and most likely diagnosis.

For the next layer of reasoning, the system is designed to leverage a pretrained PyTorch-based model to analyze input from the previous layer and produce diagnostic explanations, complete with system evaluation metrics. The system processes the inputs through a trained model (described above) and tokenizer. Open-source model BERT-base-uncased, a standard 110M-parameter pretrained model, is utilized, which provides textual reasoning aligned with observed symptoms in the previous layer. For each question in the checklist, the system offers explanations, linking and highlighting the detected patterns (cues) to the corresponding PHQ-9 criteria. Diagnostic reasoning is contextualized, ensuring that the level of depression aligns with PHQ-9 standards and observed symptom patterns, which diminishes the occurrence of hallucinations. Sample system input, output, and prompt formats are presented in Figure 5.

The attribution layer matches and attributes system diagnoses against SNOMED CT terminology in real time using both regular expression filtering and semantic embeddings. The system is designed to enhance the precision of diagnosis-to-concept mapping by leveraging Sentence Transformers for semantic similarity calculations. The input from the previous layers is tokenized and converted to lowercase to improve matching accuracy. The diagnosis is divided into keywords, and a regex pattern is constructed. The “term” column in the SNOMED dataset is filtered for matches containing any of the keywords, ensuring broader term coverage. If multiple terms match, the system uses Sentence Transformers to compute embeddings for the matched terms and the diagnosis. Cosine similarity scores are calculated to identify the term most semantically similar to the diagnosis input. Once the closest match is determined (presented as a similarity score), the system extracts and returns relevant information, including “moduleId,” “conceptId,” “languageCode,” and the matched SNOMED concept. A detailed message is included in the output to enhance interpretability depicted in Table 1.

System testing is carried out on a subset (44 samples) of manually validated diagnosis-to-SNOMED mappings. The combination of regex filtering and semantic embeddings ensures improved accuracy. We believe that with the proposed enhancements, the system can become a highly reliable tool for mapping clinical data. To support clinical deployment, the system reports the following: average inference time per input, 38 ms on an NVIDIA T4 GPU; end-to-end latency (preprocessing → reasoning), 112 ms; and peak memory usage during inference, 1.3 GB. The code is made available as open source on https://github.com/Sweenderella/MATRIX for ease of reproducibility.

The implemented system provides a comprehensive framework for generating, classifying, and explaining PHQ-9 checklists. By leveraging advanced language models such as DeBERTa and RoBERTa, the system demonstrated state-of-the-art performance in aligning AI-generated outputs with established clinical standards. Rigorous evaluations using the PRIMATE dataset highlighted its ability to generate accurate, reliable, and interpretable diagnostics, making it a transformative tool in mental healthcare. The integration of standardized assessments, such as PHQ-9, ensures diagnostic consistency, while mapping AI-driven reasoning to real-time SNOMED CT concepts fosters clinical relevance and actionable insights. This capability not only reduces diagnostic time but also enhances trust of clinicians in AI-driven recommendations, ultimately supporting more efficient workflows and improved patient outcomes.

By addressing critical challenges in mental health diagnostics, the system exemplifies the potential of X-AI to bridge the gap between technical innovation and clinical application while maintaining the privacy of users’ data. Its scalable and accessible design positions it as a valuable resource in addressing the growing demand for mental health services, particularly in underresourced and geographically dispersed populations. Moreover, the novel MATRIX interface enables real-time interaction and reasoning, enhancing both patient engagement and clinician efficiency. The study’s emphasis on transparency, accuracy, and semantic reasoning ensures that the system not only delivers clinically reliable outputs but also supports practitioners in delivering high-quality care in diverse healthcare environments.

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.