Artificial intelligence and major depression: Toward mechanistic and clinically actionable models

Abstract

Major depressive disorder is a widespread psychiatric disorder driven by complex genetic, neurobiological, psychological, and environmental mechanisms. Conventional diagnostic systems, such as the Diagnostic and Statistical Manual of Mental Disorders, the Fifth Edition and International Classification of Diseases, rely on symptom-based evaluations, which are limited by subjectivity, symptom overlap, and restricted applicability across diverse populations. Advances in artificial intelligence (AI) provide new opportunities for objective, data-driven depression assessment. This review synthesizes epidemiological, etiological, and clinical evidence to evaluate AI-based approaches for depression detection and characterization. Machine learning, deep learning, and large language model-based methods applied to multimodal data, including electronic health records, neuroimaging, electroencephalography (EEG), speech and language data, and digital behavioral signals, were systematically examined, with particular attention to interpretability and ethical considerations. Depression was consistently associated with monoaminergic and neurotrophic dysregulation, inflammation, hypothalamic pituitary adrenal axis dysfunction, and frontolimbic network abnormalities. AI models demonstrated strong discriminative performance using biological and behavioral markers, particularly when multimodal data integration was employed. Neuroimaging and EEG analyses revealed network-level alterations, while natural language processing approaches captured linguistic and acoustic markers linked to symptom severity and suicide risk. AI-based systems have substantial potential to advance precision psychiatry by enabling earlier detection and personalized treatment of depression. However, challenges, including dataset bias, methodological heterogeneity, limited interpretability, and insufficient real-world validation, must be addressed through standardized, transparent, and ethically guided clinical research.

Key Words: Major depressive disorder; Artificial intelligence; Computational psychiatry; Machine learning; Multimodal data integration; Precision psychiatry

Core Tip: This review synthesizes contemporary evidence showing that artificial intelligence is reshaping the scientific and clinical understanding of major depressive disorder by connecting epidemiological patterns, etiological mechanisms, and neurobiological findings with advanced computational models. Unlike traditional symptom-based diagnostic systems, artificial intelligence (AI)-driven approaches integrate multimodal data - including neuroimaging, electroencephalography (EEG), speech, language, behavioral traces, and clinical records - to generate mechanistic insights, stratify patient risk, and support individualized treatment planning. The review highlights how graph-based neuroimaging models, deep learning analysis of EEG time - frequency signatures, and large language models for clinical narrative interpretation collectively form a new computational framework for precision psychiatry. It also underscores the key challenges - such as data heterogeneity, cultural bias, privacy risks, and limited real-world validation - that must be addressed to translate AI systems into trustworthy and clinically actionable tools.

INTRODUCTION

Major depressive disorder (MDD) represents a major global mental health challenge, as it is characterized by persistent depressive mood, enduring feelings of sadness, diminished interest in previously pleasurable activities, and substantial impairment in daily functioning. Epidemiological evidence indicates that MDD affects approximately 16.2% of individuals across their lifetime and 6.6% within any given 12-month period[1,2]. Its classification as a serious public health concern stems from the considerable disease burden it imposes across populations worldwide[1,3]. Historically, the diagnostic process for depression evolved from descriptive psychopathology to more structured and standardized approaches, culminating in the development of operationalized diagnostic systems, such as the Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification of Diseases (ICD), which provide criteria for identifying depressive syndromes[1,4,5]. Despite these advances, current diagnostic practices face several limitations, including the need for clinical assessment, symptom overlap with other psychiatric conditions, and persistent inequalities in access to mental health services, particularly among vulnerable and underserved groups[6,7].

The expanding availability of diverse digital data sources has created a methodological foundation that enables the development of artificial intelligence- (AI) based systems for the detection and monitoring of depression[2,7]; AI systems function as unbiased tools that may improve existing assessment systems to detect problems early and track population changes and risk assessment[8-10]. The field now recognizes that computational models can detect hidden patterns that human evaluators cannot perceive in behavioral, linguistic, neurobiological and multimodal data.

The current state of depression detection systems remains immature. AI models show distinct differences because they use different data sources, computational frameworks, and evaluation methods, and deliver different medical benefits. The current studies face restrictions in their ability to generalize results because they use limited diverse samples, show cultural and linguistic prejudices, and employ different research approaches[2,11,12]. The scientific community continues to debate depression detection methods and modeling approaches because researchers want to study social media-based linguistic and behavioral signals[13-15], while others support physiological and neuroimaging and connectomic biomarkers as more biologically relevant alternatives[16,17]. AI systems encounter various barriers for clinical deployment because of privacy risks, discriminatory algorithms, unclear model operations, and inadequate testing in actual healthcare settings[18,19]. The current AI system limitations create two major risks that could worsen social inequality and produce medical errors, resulting in delayed critical medical procedures[4,8].

In this review, we introduce a conceptual framework that explains depression as a multifaceted condition that includes demographic patterns and disease origins and treatable symptoms, which can be assessed through DSM-5 and ICD-10 diagnostic criteria[1,3,20]. The framework allows AI-based detection models to process multiple data types through machine learning (ML) and deep learning (DL) algorithms, which analyze text information, behavioral data, physiological signals, neuroimaging data, and biosignals to detect depressive symptoms and their severity and risk factors[21-23]. Researchers use clinical symptoms together with computational methods to perform systematic assessments of AI system diagnostic accuracy, interpretability, and medical usefulness.

METHODOLOGY

The current evidence about AI-based methods for MDD detection and characterization has been combined in this review. The research uses a narrative method instead of systematic reviews to create an extensive conceptual framework that unites data from epidemiology, etiology, clinical practice and computational science. The research team conducted database searches through PubMed, Web of Science, IEEE Xplore and Google Scholar to find articles published between 2010 and 2024. Key search terms included combinations of: “Major Depressive Disorder”, “depression”, “artificial intelligence”, “machine learning”, “deep learning”, “neuroimaging”, “EEG”, “natural language processing”, “large language models”, “biomarkers”, and “computational psychiatry”. The research focused on studies that demonstrated the utility of AI with different types of data, including medical files, brain images, brain wave recordings, speech, behavioral information, and methods that combine multiple data sources. Methodological papers and clinical validation studies were included to understand the complete range of AI applications that exist for MDD treatment. The narrative synthesis method enabled the detailed evaluation of different research approaches while identifying gaps in knowledge, which guided the development of new research paths.

The study evaluates present evidence about AI-based depression detection systems through their connection to epidemiological elements, etiological factors, clinical symptoms, and diagnostic criteria. The study evaluates current detection methods through performance testing to establish diagnostic standards and identify current strengths and weaknesses of AI systems. The review examines ML, DL, and large language model (LLM)-based detection systems, which process multiple data types. This review fills critical knowledge gaps about data integration, model interpretability, and deployment readiness to develop AI systems that provide substantial clinical value at large scale with ethical standards for early disease detection and individualized medical treatment.

EPIDEMIOLOGY

Epidemiological evidence indicates that MDD has a point prevalence of approximately 4.7% in the general population, with multiple studies demonstrating a progressive increase in depressive disorder prevalence over time[24]. The World Health Organization (WHO) states that depression affects 4% of the global population, with 5.7% of adults having depression (4.6% men and 6.9% women) and 5.9% of people aged 70 and older[25]. The WHO shows that MDD affects women 1.5 times more than men, and postpartum and pregnant women experience MDD at rates above 10%[26].

Research findings about MDD incidence and prevalence are inconsistent because different study approaches and participant groups were used[24]. Previous studies found that men developed MDD at a rate of 3.2% per year, while women developed the condition at 4.9% per year[27]. The World Mental Health Survey Consortium conducted population-based studies across 28 countries using a standardized protocol and the WHO Composite International Diagnostic Interview to identify MDD cases[27]. Findings revealed regional differences in 12-month prevalence, with rates of 5.5% in high-income countries and 5.9% in low- and middle-income countries[24,27].

Prospective studies have suggested that the lifetime prevalence of MDD may exceed 30%[28,29]. Systematic reviews and multinational studies estimate the 1-year prevalence at 4%-5%[24,30]. Although prevalence rates differ across countries, developing nations tend to report higher rates[24,29,30]. However, the extent to which these differences reflect true epidemiological variation remains unclear due to potential influences, such as cultural factors, stigma, and methodological inconsistencies[29,30].

When examined by sex, women have nearly twice the risk of being diagnosed with MDD[31]. A recent meta-analysis confirmed that MDD is more common in women across all age groups[32]. Some studies have shown symptom clustering by sex: Depressed mood, psychomotor retardation, and pessimistic thoughts are more frequent in women, whereas anger outbursts, irritability, risky behaviors, and suicide attempts are more frequent in men[33]. Consistent with DSM-5, suicide attempts are more common among women, while suicide deaths are more common among men[34,35]. DSM-5 also notes that atypical features, such as hyperphagia and hypersomnia, are more prevalent in women, who also tend to exhibit interpersonal sensitivity and gastrointestinal symptoms. Men, on the other hand, more often adopt maladaptive coping strategies, including substance misuse and impulsive behaviors[35]. Regarding age, MDD can occur across the lifespan. Studies highlight a peak onset between 15 years and 20 years in younger populations[36], although high prevalence rates are also documented among older adults[37-39]. MDD continues to be a significant public health issue because it affects people of all ages and causes substantial illness, death, and work performance decline[40].

The number of American adults who diagnosed with MDD rose by 12.9% between 2010 and 2018 to reach 17.5 million from 15.5 million. The number of 18-34-year-olds diagnosed with MDD increased from 34.6% to 47.5% during the same time period, and the economic costs of MDD treatment expanded to 326.2 billion USD. The majority of MDD-related expenses stemmed from workplace-related expenses, including employee absence, reduced work performance, and job loss[41]. A meta-analysis similarly reported escalating costs over time due to inpatient care, outpatient services, pharmacotherapy, emergency visits, unemployment, and suicide-related expenses[42].

In 2021, an estimated 727000 people worldwide died by suicide, making it the third leading cause of death among individuals aged 15-29[25]. Suicide attempts and suicide-related mortality substantially contribute to the morbidity and economic burden of MDD[43]. The lifetime risk of suicide attempt in individuals with MDD is approximately 28%, underscoring the importance of prevention, early identification, and effective treatment in reducing suicide risk[43,44].

ETIOLOGY

Although the etiology of MDD has not been fully elucidated, it is widely accepted to be multifactorial and complex. Genetic factors, gene-environment interactions, environmental exposures, neurobiological mechanisms, inflammation, perinatal complications, stressful life events, and traumatic experiences all appear to contribute to risk[45]. No specific gene locus has been definitively linked to MDD, likely because: (1) No single gene is necessary or sufficient for the disorder; (2) Each susceptibility gene contributes only a small portion of the overall genetic risk; and (3) Extensive genetic heterogeneity may predispose individuals to clinically indistinguishable syndromes[46]. Heritability estimates for MDD range from 31%-42%[47]. Rather than a single gene or allele, thousands of variants may exert small cumulative effects[46,47]. Increasing attention has shifted toward gene-environment interactions, particularly childhood adversity, neglect, abuse, and stressful life events, all of which significantly influence the risk of developing MDD. These concepts correspond to the classic stress-diathesis framework[48]. People who have lower education levels and lower socioeconomic status tend to develop more depressive symptoms[49]. The severity of depressive symptoms results in job loss, which creates new economic problems for people who experience depression[50,51]. People who experience traumatic events become more likely to develop psychiatric conditions in addition to their MDD[52,53].

Multiple studies have demonstrated the biological origins of depression[54-62]. The monoamine hypothesis stands as a leading theory to explain how depression develops through changes in serotonin (5-HT), norepinephrine (NE), and dopamine (DA) levels[54]. Studies show that MDD patients have decreased serotonin metabolite levels in their bodies[54,55]. The various functions of serotonin receptors (5-HT1-5-HT7) result in the development of MDD[56,57]. The serotonergic system affects how patients follow their treatment plans, how well they respond to medication, how quickly their symptoms improve, and their resistance to treatment[58]. Reduced NE levels, altered NE synthesis and degradation, and receptor-level dysregulation have all been associated with MDD[58-60]. DA dysfunction, involved in reward processing, motivation, and hedonic capacity, is likewise implicated[58,61]. These systems closely interact; dysregulation in one can adversely impact the others, ultimately contributing to psychopathology[58,62].

Molecular studies have identified three major MDD-associated biological pathways: (1) Reductions in neurotrophic and peripheral growth factors [e.g., brain-derived neurotrophic factor (BDNF), vascular endothelial growth factor, insulin-like growth factor-1]; (2) Elevations in pro-inflammatory cytokines (e.g., interleukin-1β, interleukin-6, tumor necrosis factor-α); and (3) Dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis[63].

Neuroimaging studies consistently show decreased metabolic activity in the dorsolateral and ventral prefrontal cortex, a well-established neuroanatomical feature of MDD. Dysfunction within prefrontal-subcortical circuits has been linked to symptoms such as psychomotor retardation[64]. Other studies have reported significant gray matter reductions in the anterior cingulate cortex (ACC), which appear to correlate with the recurrence of depressive episodes[65]. Functional imaging demonstrates heightened amygdala, ventral striatal, and medial prefrontal cortex responses to negative emotional stimuli, alongside reduced ventral striatal activation in response to positive stimuli[66]. Additionally, inflammatory markers have been associated with disease severity, treatment response, and suicide attempts[67,68]. Some inflammatory markers may also influence anger regulation and consequently contribute to self-harm or outwardly directed aggression[69,70].

Furthermore, it has been shown that MDD, with its complex and difficult-to-understand etiology, involves not only a mental psychopathology but also environmental ecological models[71]. Presented as a new perspective in the fields of health and psychiatry, the ecological model provides a very important perspective for MDD, both in terms of etiology and treatment[72]. This model suggests that any disruption or imbalance in the ecological environment with which a person is constantly interacting can contribute to the onset and maintenance of mental illnesses. These models also predict that interventions may have more lasting effects on treatment[73].

The framework shows how etiological factors lead to neurobiological mechanisms, which result in AI-based precise psychiatric treatment (Figure 1). The first layer (etiological factors) contains three categories, which include genetic factors (31%-42% heritability and multiple genetic risks), environmental stressors (adverse childhood experiences and chronic stress), psychosocial elements (SES and social isolation) and medical conditions. The neurobiological mechanisms section at layer 2 presents six distinct but converging pathways. These include monoamine dysregulation of 5-HT, NE and DA, neurotrophic impairment of BDNF, neuroplasticity and inflammatory activation of cytokines, microglial and HPA axis dysfunction of cortisol, stress response and network disruption of default mode network (DMN) and frontoparietal and regional brain changes of dorsolateral prefrontal cortex, ACC, and hippocampus. The third layer (biomarkers & data sources) detects quantifiable indicators that exist in neuroimaging data [functional magnetic resonance imaging (MRI), structural MRI (sMRI), diffusion tensor imaging (DTI), positron emission tomography], electrophysiology data [electroencephalography (EEG), transcranial magnetic stimulation (TMS)-EEG], molecular markers (cytokines, genomics, metabolomics), and behavioral/Linguistic characteristics (speech, digital phenotyping). The fourth layer (AI modeling approaches) demonstrates how computational methods analyze specific biological processes through Graph Neural Networks for connectomic analysis and DL (CNN/RNN) for imaging pattern recognition, time-series analysis for EEG signals, multi-omics integration for systems biology, natural language processing (NLP) for linguistic marker detection, and causal inference for mechanistic discovery. The fifth layer (clinical outcomes) demonstrates how precise psychiatric care becomes possible through its ability to achieve 85%-95% diagnostic accuracy and 78%-86% treatment selection precision, and its capacity to detect conditions early and predict treatment results. The integration panel explains that doctors can understand depression mechanisms through multiple AI systems, enabling them to develop personalized treatment strategies that produce improved patient outcomes.

Figure 1 From etiological factors to artificial intelligence-driven precision psychiatry: A mechanistic framework for depression. AI: Artificial intelligence; PRS: Polygenic risk scores; GWAS: Genome-wide association studies; ACEs: Adverse childhood experiences; CVD: Cardiovascular disease; BDNF: Brain-derived neurotrophic factor; VEGF: Vascular endothelial growth factor; IGF-1: Insulin-like growth factor-1; NGF: Nerve growth factor; IL: Interleukin; CRH/ACTH: Corticotrophin-releasing hormone/adrenocorticotropic hormone; DMN: Default mode network; DLPFC: Dorsolateral prefrontal cortex; ACC: Anterior cingulate cortex; SCM: Structural causal models; DAG: Directed acyclic graphs; NLP: Natural language processing; MFCC: Acoustic feature extraction; CRP: C-reactive protein; SNPs: Single nucleotide polymorphisms; EEG: Electroencephalography; ERPs: Event-related potentials; TMS: Transcranial magnetic stimulation; REM: Rapid eye movement; fMRI: Functional magnetic resonance imaging; sMRI: Structural magnetic resonance imaging; DTI: Diffusion tensor imaging; PET/SPECT: Positron emission tomography/single-photon emission computed tomography; MDD: Major depressive disorder; PTSD: Post-traumatic stress disorder; ECT: Electroconvulsive therapy; GCN: Graph Convolution Network; GAT: Graph Attention Network; TRD: Treatment-resistant depression.

CLINICAL FEATURES AND DIAGNOSTIC CRITERIA

MDD is characterized by a persistently depressed mood, markedly diminished interest or pleasure, and significant functional impairment lasting for at least 2 weeks and occurring nearly every day[74]. The condition produces additional symptoms that affect attention and concentration, cause sleep problems and changes in appetite, psychomotor slowdown, and feelings of guilt and suicidal thoughts that may lead to suicide attempts[75]. The DSM-5 diagnostic framework for MDD appears in Figure 2, which demonstrates the multiple aspects of this condition. The figure shows how symptom thresholds, temporal requirements, and exclusion criteria cooperate to achieve precise diagnostic results in clinical practice. The diagnostic criteria for MDD require more than affective symptoms because they involve multiple cognitive, behavioral, somatic, and functional impairments and thorough differential diagnosis evaluation. This schematic representation reinforces the importance of distinguishing MDD from medical conditions, substance-related states, and bipolar spectrum disorders, thereby supporting a more accurate and evidence-based diagnostic process.

Figure 2  Diagnostic and Statistical Manual of Mental Disorders-5 diagnostic criteria for major depressive disorder[69,70].

According to DSM-5 diagnostic specifications, several specifiers help determine the severity and longitudinal course of MDD. These include mild, moderate, or severe episode presentation; the presence or absence of psychotic features; whether the episode is isolated or recurrent; and whether full remission is achieved. DSM-5 further delineates specifiers such as anxious distress, mixed features, melancholic features, atypical features, and mood-congruent or mood-incongruent psychotic features for MDD[69,70].

TREATMENT

Antidepressant medications constitute the first-line treatment for MDD. Second-generation antidepressants, such as selective serotonin reuptake inhibitors (SSRIs), serotonin-NE reuptake inhibitors (SNRIs), and other agents selectively modulating neurotransmitter activity play a central role in current clinical management. Although these medications demonstrate similar efficacy to first-generation agents such as tricyclic antidepressants and monoamine oxidase inhibitors, they are preferred due to their more favorable side-effect profiles and substantially lower toxicity risk in overdose[76]. Contemporary treatment guidelines recommend therapeutic-dose administration for 6-12 weeks[76,77]. Approximately 60% of patients with MDD respond to second-generation antidepressants[78,79]. For non-responders, augmentation strategies, antidepressant combinations, antipsychotic augmentation, or mood stabilizers may be required[79]. Higher-dose SSRI use has been shown to improve treatment response rates in MDD[80], with comparable findings reported for SNRIs[81]. Optimizing medication dosage and duration, combining antidepressants with non-antidepressant agents, using dual antidepressant regimens, or switching to an alternative antidepressant are among the strategies used to enhance efficacy. When pharmacological treatments are insufficient or ineffective, psychosomatic interventions and individualized psychotherapeutic approaches become essential components of care[82].

Electroconvulsive therapy

Electroconvulsive therapy (ECT) induces a controlled epileptic seizure through electrical stimulation of the brain and has proven highly effective for severe psychiatric conditions. In MDD, ECT has demonstrated efficacy comparable to pharmacotherapy in reducing depressive symptoms[83]. It remains particularly valuable for severe or treatment-resistant cases, individuals unresponsive to medications, and pregnant patients for whom pharmacotherapy may be contraindicated[84].

TMS

TMS is a non-invasive neuromodulation technique widely used in the treatment of MDD[85]. Magnetic pulses are administered without causing pain or discomfort, and the primary therapeutic aim is to reduce depressive symptoms[85,86]. TMS has gained prominence due to its safety, tolerability, and applicability in cases where pharmacotherapy alone is insufficient.

Deep brain stimulation

Deep brain stimulation (DBS) is an important intervention in treatment-resistant depression (TRD). DBS exerts its antidepressant effects by modulating specific neural circuits, including the nucleus accumbens, ventral capsule/ventral striatum, subgenual ACC, lateral habenula, inferior thalamic peduncle, medial forebrain bundle, and stria terminalis[87]. Its use reflects growing recognition of MDD as a network-level disorder involving disrupted mood-regulating circuits.

Individualized psychotherapeutic approaches

Interpersonal therapy, behavioral therapy, cognitive-behavioral therapy, metacognitive therapy, and problem-solving therapy have all demonstrated efficacy either as monotherapies or in combination with pharmacotherapy. Notably, the addition of cognitive therapy to pharmacological treatment has been shown to reduce relapse rates[88].

AI MDD treatment approaches

In recent years, AI-assisted treatment models have been particularly studied. In a recent study in this field, the AI system was found to be quite effective in predicting, increasing, and monitoring the effectiveness of pharmacological treatment. In addition, the system provided very important data in evaluating suicide risk and ensuring the patient's safety in this regard[89].

COURSE, OUTCOMES, PROGNOSIS, AND CHALLENGES IN CLINICAL APPLICATION

Research findings show that MDD without treatment produces adverse health effects that require early patient identification for proper treatment initiation. Early treatment response, rapid remission, and higher premorbid functioning predict a more favorable prognosis[90]. The presence of psychiatric or general medical comorbidities negatively affects disease trajectory[91]. Clinical risk factors for poor prognosis include moderate to severe depression, recurrent episodes, suicidal ideation or behavior, psychotic features, family history of depression, and high levels of accompanying anxiety. Comorbid substance use disorders, personality disorders, panic disorder, and post-traumatic stress disorder (PTSD) also worsen the prognosis[92]. Demographic risk factors include young or advanced age, female sex, being single, living alone, and unemployment[93]. Physical comorbidities such as cardiovascular disease, chronic pain conditions, thyroid dysfunction, diabetes, and autoimmune illnesses are likewise associated with poorer outcomes[92,93].

The fact that MDD is diagnosed based solely on symptoms, the absence of a biological marker, the diverse clinical presentations of patients, and the overlap of symptoms with other psychiatric disorders such as bipolar disorder and PTSD can lead to misdiagnosis[94]. Although neuroimaging, EEG, and research on inflammatory markers are promising for diagnosis, the high cost and time-consuming nature of these tests, the lack of standardization in this area, and the absence of a biological test that can be used in daily practice are some of the difficulties experienced in terms of diagnosis and treatment follow-up in MDD[95]. The third major challenge is the low response rate to antidepressants in first-line treatment, and the lack of biomarkers that can predict treatment response. This can lead to treatment changes, longer treatment durations, wasted time, MDD becoming resistant, and an increased risk of suicide[96]. From a translational perspective, successful clinical implementation of AI-based tools will require standardized data acquisition protocols, prospective validation in real-world clinical settings, seamless integration into existing clinical workflows, and clear regulatory and ethical governance. Without these prerequisites, AI systems risk remaining research tools rather than becoming reliable clinical decision-support instruments.

Given the substantial public health impact of MDD stemming from loss of productivity, labor-force reductions, treatment expenditures, and elevated suicide rates, rapid diagnosis and appropriate treatment planning are essential for reducing societal and economic burden[90-93,97].

AI-BASED APPROACHES IN DEPRESSION AND FUTURE PERSPECTIVES

The complex nature of depression, which includes biological, psychological, and social elements, makes it difficult for traditional clinical assessment methods to understand the complete extent of the disorder. AI serves as an essential epistemological instrument to help doctors diagnose depression, predict treatment results, and identify patients at risk for depression. AI systems demonstrate their ability to enhance medical diagnosis while scientists gain better understanding of depression brain functions and develop new biomarkers and individualized psychiatric interventions[11,16,20,98,99].

The framework shows the entire AI-based system, which detects depression and identifies its characteristics. The data sources contain different types of information, including medical documents, brain imaging data from functional MRI (fMRI), sMRI, DTI, EEG and TMS-EEG recordings, text and speech data, wearable device tracking data, and social media usage records. The AI methodologies section describes the computational methods, including traditional ML algorithms (Random Forest, SVM, XGBoost), sophisticated DL systems (CNN, LSTM, Transformers), graph neural networks for connectomic analysis, NLP models (BERT, GPT-based LLMs), and multimodal fusion techniques. The AI outputs show how the system produces different clinical findings, which include diagnostic results with 70%-95% accuracy in studies, severity evaluations, treatment outcome forecasts, suicide risk evaluation, depression subtype detection, and new biomarker identification. The clinical application section shows how AI-produced results transform into medical procedures, which include screening programs, early detection methods, precision psychiatry treatment plans, clinical decision systems, prognosis evaluation, healthcare services for disadvantaged groups, and ongoing patient tracking. The workflow starts with raw data acquisition followed by computational analysis before AI systems become ready for clinical use, according to the unidirectional arrows, which show the translation process of AI systems in contemporary psychiatric care (Figure 3).

Figure 3 Comprehensive artificial intelligence framework for depression detection: From data sources to clinical application. EHRs: Electronic health records; fMRI: Functional magnetic resonance imaging; sMRI: Structural magnetic resonance imaging; DTI: Diffusion tensor imaging; EEG: Electroencephalography; TMS: Transcranial magnetic stimulation; AI: Artificial intelligence; SHAP: SHapley Additive exPlanation; MDD: Major depressive disorder; PHQ-9: Patient Health Questionnaire 9; TRD: Treatment-resistant depression.

The rise of AI-based diagnostic classification approaches

ML and DL techniques help doctors make better depressive disorder diagnoses through their ability to reduce human errors and remove personal biases. AI models that process clinical assessments, electronic health records (EHRs), demographic information, behavioral markers, and digital communication records produce superior diagnostic results compared with traditional psychometric tests[2,100]. The performance of classical ML algorithms, including Random Forest, SVM, and ensemble methods, is strong in large datasets, yet DL architectures, including CNNs, LSTMs, Transformers, and hybrid models, demonstrate better ability to detect intricate patterns in high-dimensional data[11].

The current models face multiple limitations because they operate with restricted sample sizes and insufficient cultural representation, and they use cross-sectional data, which hinders their ability to generalize[20]. Diagnostic AI systems for psychiatric use need validation studies, which evaluate their performance across different patient populations with sufficient participant numbers.

Neuroimaging-based AI: The emergence of connectomic biomarkers

Scientists have achieved major breakthroughs in depression research through biological studies because AI models based on neuroimaging data now exist for biological investigations. The combination of GNNs with spatiotemporal CNN/RNN architectures and Transformer-based models applied to fMRI, sMRI, and connectomic data allows researchers to accurately identify depression-related connectivity problems[101-103]. Research findings show that depression affects essential neural pathways, which include the frontoparietal control network, limbic system, DMN, and paralimbic regions. The models show 70%-85% accuracy in remission prediction, which indicates their ability to help optimize treatment plans for individual patients[104-106]. The implementation of neuroimaging-based AI approaches faces multiple obstacles, which include restricted sample sizes, expensive data collection, inconsistent data processing, and different scanner types. The development of future progress depends on building large-scale multicenter datasets that use standardized acquisition methods[107,108].

EEG-based DL models: The role of time-frequency biomarkers

EEG stands as a non-invasive and cost-effective method that provides high temporal resolution for studying depression and treatment responses. The combination of DL models with CNNs, transformers, and hybrid systems enables researchers to achieve 85%-95% accuracy in depression diagnosis through their analysis of time-frequency and connectivity-based EEG features[109-112]. The combination of TMS-EEG with resting-state EEG produces high discriminative power for identifying clinical subtypes, including TRD[113,114]. EEG study reproducibility faces multiple obstacles because researchers use different methods to place sensors and handle artifacts, and they often work with insufficient participant samples[115].

NLP and LLMs: Computational analysis of clinical language

Research analysts today employ NLP and LLMs to perform fast analysis of clinical interview data, free-text, and speech information. The application of domain-adapted BERT and GPT derivatives and similar LLMs to linguistic data enables high-accuracy predictions of symptom severity, suicidal risk, and cognitive dysfunction indicators[116-118]. LLMs show outstanding capability to identify concealed semantic and syntactic patterns that typical clinical evaluation methods fail to detect. The implementation of LLMs encounters various obstacles because they produce fabricated information, show cultural biases, disclose personal data, and generate untrustworthy results in unpredictable situations[119,120]. Decision support systems that use NLP require complete clinical validation to achieve readiness for real-world deployment.

Multimodal AI frameworks: A new paradigm in computational psychiatry

The development of unified AI systems that combine clinical data with neuroimaging results, EEG recordings, behavioral information, speech and textual data, and wearable sensor readings enables the creation of "computational phenotyping" for depression research. Multimodal AI models, which unite different biological and behavioral signals generate better diagnostic and prognostic results than systems that use individual signals[121-123]. The XGBoost and GNN-based classifiers show their practical value in medical practice because they successfully predict TRD risk[124]. The current limitations of multimodal fusion stem from inconsistent data processing and different measurement resolutions between modalities and insufficient large-scale harmonized datasets.

Figure 4 illustrates the multimodal AI integration framework for computational phenotyping of MDD. The top section (data modalities) presents five different data types that work together as a single system. The neuroimaging data include fMRI connectivity, sMRI volumetry, and DMN disruption information. The EEG signals contain three different types of data, including time-frequency patterns, network connectivity, and TMS-EEG responses. The system includes clinical data consisting of EHRs, symptom rating scales, and demographic information. The system contains language data, including semantic features, acoustic patterns, and syntactic markers. The system contains behavioral data, including activity patterns, social media, and wearable sensor information. The middle section (fusion strategies) contains four different fusion methods, which perform early fusion through uniting feature-level data, late fusion through decision-level data union, hybrid fusion for multi-stage integration, and attention-based weighted modality fusion. Multimodal AI systems achieve better results through their ability to unite different biological and behavioral indicators, which leads to enhanced depression diagnosis, treatment, and prognosis evaluation.

Figure 4 Multimodal artificial intelligence integration. fMRI: Functional magnetic resonance imaging; sMRI: Structural magnetic resonance imaging; DMN: Default mode network; EEG: Electroencephalography; TMS: Transcranial magnetic stimulation; AI: Artificial intelligence; EHRs: Electronic health records; MDD: Major depressive disorder.

Ethics, interpretability, and clinical translatability

Multiple essential obstacles exist for AI technology implementation in psychiatric practice because of model interpretability problems, privacy risks, demographic bias, and inadequate clinical testing standards. The DL and LLM architectures require interpretability solutions, which include SHapley Additive explanation, LIME, attention maps, class activation mapping, and uncertainty quantification methods to explain their decision-making processes[20,118,124].

Organizations need to establish sustainable AI systems through the use of federated learning, fairness-aware algorithms, diverse datasets and strong regulatory frameworks[119,125,126]. AI systems should continue to preserve existing health disparities when no protective systems are in place instead of using their capabilities to reduce these inequalities. Future studies need to use standardized research methods that combine with cultural adaptation algorithms, extensive long-term data collection, and uniform assessment procedures for both diagnosis and validation. Transparent reporting practices and prospective multi-center validation studies will be essential to ensure that AI systems are technically robust, generalizable across populations, and clinically meaningful.

Most AI-based tools for MDD exist in the research and pilot testing stages instead of being used in standard clinical practice. The systems demonstrate potential for identifying initial symptoms, forecasting suicide potential, and treatment effectiveness, yet they operate as screening tools to assist healthcare professionals instead of autonomous clinical decision-making systems. The current restricted deployment of early detection systems, treatment guidance, and long-term care management exists because of insufficient prospective testing, missing regulatory clearance, healthcare system integration, and medical staff acceptance. AI systems in MDD treatment serve as additional tools for healthcare providers to employ to support their professional decisions, but they do not substitute medical expertise. The technology requires additional research to prove its effectiveness for medical practice in typical clinical settings.

Future perspectives: The direction of computational psychiatry

The current research demonstrates that AI in depression studies has evolved into a single system that combines multiple analytical levels with biological principles. The field of computational psychiatry will progress through the development of system-level models that unite neurobiological elements with cognitive and behavioral aspects of depression for unified analysis. Researchers need to work with large datasets with cultural diversity to develop new models while building multimodal AI systems, using causal modeling, and creating explainable algorithms. The field will transition toward models that perform causal inference to transform depressive pathology correlations into mechanistic explanations. The models enable healthcare providers to identify specific biological and behavioral factors that impact treatment outcomes for better AI-based decision support system clinical value.

The future development of AI systems depends on upholding ethical principles that serve as their fundamental base. The development of fair, private, and transparent AI systems remains essential because it helps prevent algorithmic bias and maintains patient trust. AI systems require extensive testing across multiple real-world clinical settings to demonstrate their ability in treating patients with complex medical needs and diverse diagnostic characteristics. The future of AI depression treatment will unite extensive data systems with complex computational systems, explainable models, and real-world clinical testing methods. The proposed method will create better depression detection methods and personalized treatment plans, which will transform current psychiatric medical practices.

Figure 5 presents the obstacles that block AI-based depression research, while showing possible research paths. The four main obstacles include: (1) Data-related problems stemming from insufficient sample diversity, cultural prejudices, inconsistent data preparation, and insufficient multimodal dataset standards; (2) Methodological problems stemming from different diagnostic standards, multiple feature extraction techniques, restricted applicability, and insufficient future testing; (3) Ethical and privacy problems that include algorithmic bias, privacy risks, insufficient transparency, and potential healthcare inequalities; and (4) The process of moving research to clinical practice faces three main obstacles, including insufficient real-world testing, difficulties with workflow implementation, and delays in obtaining regulatory approval. The strategic directions that will guide future development are in panel B (future perspectives). Scientists require new methods to study depression pathophysiology because correlation-based research methods produce insufficient results. The second strategic direction aims to create XAI methods, which will generate complex explanations that medical personnel can understand. Studies need to validate their findings by integrating data from multiple testing sites. The combination of clinical practice with precision psychiatry enables doctors to provide personalized medical care, which includes ongoing patient monitoring. The development of ethical AI systems together with governance frameworks will defend privacy rights while building equitable systems and robust regulatory frameworks. The bridge section shows that clinical translation success requires equal advancement of all four essential elements which include established datasets and protocols and clear models with explainable operations and thorough clinical testing and ethical oversight systems. The integrated method represents a core necessity for developing AI-based depression research into dependable tools that provide equal benefits to all patients and generate superior treatment outcomes and enhance precision psychiatry.

Figure 5 Current challenges and future perspectives in artificial intelligence-based depression research. FDA: Food and Drug Administration; AI: Artificial intelligence; SHAP: SHapley Additive exPlanation.

Despite promising performance metrics reported across studies, many AI-based depression models face substantial methodological limitations. Common issues include overfitting due to high-dimensional features and small sample sizes, data leakage arising from improper cross-validation, class imbalance, and limited external or cross-site validation. Furthermore, many models are trained on culturally homogeneous datasets, restricting cross-cultural generalizability and clinical transferability. These limitations indicate that reported accuracy values should be interpreted cautiously and do not necessarily reflect real-world clinical performance.

Although many AI-based studies in depression report high predictive accuracy, most rely on correlational associations rather than causal inference. Consequently, current AI models should not be interpreted as directly explaining the underlying pathophysiology of MDD. However, biologically informed and multimodal AI frameworks offer a promising pathway toward mechanistic understanding by integrating neurobiological knowledge with longitudinal and interventional data. Emerging approaches such as causal modeling, graph-based inference, and hypothesis-driven AI may enable future systems to move beyond prediction and toward mechanistic insight.

CONCLUSION

The research findings from this review show that AI serves as an effective method to enhance MDD detection and characterization through the combination of epidemiological data with etiological factors, clinical signs, and operational diagnostic systems. AI-based methods show great potential to detect complex biological and behavioral depression indicators that standard symptom assessment methods fail to detect. AI-based depression models have achieved various improvements throughout their development, yet they do not have widespread adoption in medical facilities. The research faces multiple essential obstacles that stem from different study approaches, small participant groups, absent external and prospective study validation, unexplained model complexity, and unresolved ethical and regulatory issues. Medical practitioners need to evaluate all reported performance metrics because it remains unclear if these metrics provide them with useful information. Research studies must develop standardized AI systems that analyze clinical information from large datasets containing patients from different cultural backgrounds and who have been monitored for extended periods. The combination of explainable and causal modeling techniques with DSM/ICD diagnostic standards will develop vital elements that can boost system transparency, medical trust, and clinical effectiveness. AI systems will establish themselves as dependable clinical decision-support tools through developers who perform ethical validation to help doctors detect MDD at an earlier stage and create individualized treatment approaches, which will result in better patient results.