This review follows the guidelines of the PRISMA 2020 as followed by Page et al [15]. PRISMA is an evidence-based approach to enhance transparency, reproducibility, and methodological rigor in systematic reviews. PRISMA emphasizes organized reporting at key stages such as search strategy, study selection, data extraction, and synthesis in order to minimize bias and ensure accountability.

The study selection process followed a rigorous and systematic screening process with multiple stages. Initially, a total of 2534 studies were retrieved from 5 databases (PubMed, IEEE Xplore, PsycINFO, Scopus, and Cochrane Library) using predefined search terms (read Section 2.1 for search strategy). After removing duplicates and applying exclusion criteria, 35 studies were selected for final inclusion. The selection criteria focused on empirical studies that explored the use of AI in mental health diagnosis, treatment, or patient engagement, excluding studies that were not peer-reviewed or did not focus on relevant applications.

For this review, 2 independent reviewers were selected who performed the screening or selection process. The reviewers took care of the reliability and validity of the inclusion criteria. In cases of dispute, a third reviewer was consulted to settle the dispute. The Cohen κ statistic was used to determine the level of interrater agreement. The Cohen κ score was 0.84, with substantial agreement between the reviewers in the selection of studies.

To be inclusive of interdisciplinary knowledge, this review searched PubMed (biomedical emphasis), IEEE Xplore (engineering or AI), PsycINFO (psychological literature), Scopus (multidisciplinary focus), and Cochrane Library (evidence-based treatment). This strategy ensures comprehensive representation of technical, clinical, and ethical aspects of AI in mental health, as guided by Grant and Booth [16]. Research published between January 2013 and December 2024 were considered for capturing updates in recent AI applications.

Search terms combined Boolean operators and Medical Subject Headings such as (“artificial intelligence” OR “machine learning” OR “deep learning” OR “neural network”) AND (“mental health” OR “mental disorder” OR “depression” OR “anxiety” OR “schizophrenia” OR “PTSD”) AND (“diagnosis” OR “screening” OR “treatment” OR “therapy” OR “patient empowerment” OR “self-management”) for example (“artificial intelligence” OR “machine learning”) AND (“mental health”) OR “depression” OR “anxiety” AND (“diagnosis” OR “therapy” OR “patient empowerment”). It also combined various keywords related to ethical and legal challenges such as “artificial intelligence” OR “machine learning” OR “deep learning” OR “neural network” AND “personal data” OR “ethics” OR “legal protection” OR “HIPAA.” Search terms were iteratively refined using the Sample, Phenomenon of Interest, Design, Evaluation, Research Type framework to align with qualitative and quantitative research goals [17].

In this review, only the peer-reviewed empirical studies were included to ensure methodological rigor and peer validation [18]. Moreover, the study focused on studies where AI was central to diagnosis, treatment, or patient engagement, excluding tangential uses (eg, administrative tasks). Only English language publications were searched to mitigate language bias, though this introduces potential geographic bias [19].

The study did not include nonempirical papers (ie, editorials) that lack testable hypotheses or evidence, which contravenes the empiricism principle underpinning systematic reviews. The study did not include interventions irrelevant to human mental health either.

To assess the methodological quality and potential risk of bias in the included studies, the Risk of Bias-2 for intervention trials was used. The results of these assessments were summed to classify studies into 3 categories: low, moderate, or high risk of bias. In total, 9 (25%) studies were rated as having a low risk of bias, while 21 (60%) studies were deemed to have a moderate risk of bias, and the remaining 5 (15%) studies were classified as high risk.

Descriptive data from the included studies were examined based on the publication period, origin of countries, AI modality, mental health focus, methodological quality, outcome indicators, and outcome relevance and ethics. All parameters were extracted and organized using Microsoft Excel 365, which is shown in Table 1. In total, 35 research studies were included in this systematic review. These studies were published from 2013 to 2024. The publication record increased after 2018, with 23 of 35 (66%) of the papers appearing in the past 6 years. Based upon the first-author affiliations, the majority of studies were from the United States (12/35), followed by China (7/35), the United Kingdom (5/35), Australia (4/35), and Canada (3/35). In total, these studies account for approximately 89% (31/35) of all publications. All studies were published in English, reflecting the language inclusion eligibility criterion.

As far as AI modality is concerned in past studies, the NLP on social media or clinical text dominated the field and accounts for nearly 40% (14/35) of the studies. This is followed by wearable or smartphone sensor analytics (9/35, 26%), chat-based conversational agents (7/35, 20%), and multimodal deep-learning frameworks that integrate images and text. In the end, the sensor data account for 15% of the studies. The most frequently addressed mental health conditions were major depressive disorder, which accounts for 51% (18/35), and suicide ideation or risk making 34% (12/35), with smaller but growing bodies of work on bipolar disorder, schizophrenia, and anxiety disorders.

Furthermore, methodological and reporting quality were also investigated in this review. In total, 77% (27/35) of the studies declared funding support, while only 14% (5/35) reported prospective protocol registration (eg, PROSPERO or OSF). Similarly, adherence to PRISMA 2020 was explicitly stated in 20% (7/35) of the studies. The statement of risk-of-bias assessments was present in 60% (21/35) of the papers, primarily using Risk of Bias-2 for intervention trials, Quality Assessment of Diagnostic Accuracy Studies (version 2) for diagnostic accuracy studies, or the Newcastle-Ottawa Scale for observational cohorts. Merely, 26% (9/35) of the included studies were rated as having an overall low risk of bias.

From all past studies, the performance metrics were heterogeneous. In total, 71% (25/35) reported statistically or clinically meaningful improvements in diagnostic accuracy, treatment prediction, or patient-reported outcomes compared with conventional methods. However, only 17% (6/35) explicitly addressed legal or regulatory compliance frameworks (eg, GDPR), and 20% (7/35) conducted fairness or bias audits.

AI can be used to enhance the accuracy of diagnosis and timely intervention during the treatment of mental health, which is crucial to mitigating the increasing number of untreated diseases. According to the WHO’s focus on preemptive treatment to cut down on disability-adjusted life years [1], AI-based early interventions such as chatbots offer cognitive behavioral prodromal phases. In a study conducted by Torous et al [11], the severity of depression had reduced by around 22% in weeks in some cases. Darcy et al [9] confirmed this observation and also noted that early intervention is extremely essential in such cases. However, the problems still exist including the fact that models, which are trained on the homogeneous sets of data, can overlook linguistic and demographic diversity [22]. Clinicians may sluggish evidence-based human-led care due to the excessive use of AI without their control [5]. To realize the potential of AI, future work should be based on the collection of representative data, real-world validity, and hybrid human-AI workflows, with an effort toward automation, including clinical expertise.

AI is transforming therapeutic treatments for mental health conditions through supplementing conventional treatments such as CBT and enhancing access for underprivileged groups. AI-enabled interventions like chatbots (eg, Woebot and Wysa) provide guided CBT interventions by analyzing user responses through NLP and offering real-time, evidence-based feedback [9,29]. For instance, Woebot decreased depression symptoms (Patient Health Questionnaire-9 scores) by 22% in a randomized trial through daily tracking and cognitive reappraisal exercises for users [9]. AI-enabled interventions bridge important gaps in mental health treatment. Approximately 50% of the global population does not have access to clinicians, and stigma keeps almost 60% from visiting therapists in person [1]. AI-enabled interventions overcome these impediments by being available 24/7 through the use of smartphones, making discreet and low-cost interventions to marginalized groups such as rural communities and low-income communities [11]. Shortcomings still persist; however, chatbots have some difficulties with the handling of complex emergencies (eg, suicidal intent) and cannot offer the human touch in understanding others, rendering therapeutic alliance vulnerable [30]. Models that liaise AI efficiencies with human oversight, for example, AI flagging high-risk cases for human assessment, have potential for optimizing scalability and safety [31].

AI brings mental health treatment to all levels across geographical, economic, and cultural boundaries. MindDoc and other peer support networks such as TalkLife use AI to deliver CBT mindfulness exercises that are available in 20+ languages and tailored to local idioms and social norms [23,32]. For example, the Wysa application led to improvement in anxiety symptoms (Generalized Anxiety Disorder-7 scores) by approximately 30% for some Indian people by incorporating native metaphors for stress [29]. AI also addresses staffing shortcomings: in sub-Saharan Africa, where the ratios are down to 1 psychiatrist for every 2 million people. In addition, chatbots can possibly provide some temporary help until clinical care is available [1]. Yet, algorithmic bias remains a problem; models that are trained on data tend to misinterpret non-Western forms of distress, such as Asian somatic symptom cultures [22]. To solve this, X2AI has made a multilingual chat, which translates therapeutic conversations for Arabic and Swahili speakers (it engaged people by approximately 40% more than generic tools) [25]. Despite these advancements, a significant digital divide remains; approximately 30% of low-income individuals do not own smartphones, and older populations demonstrate lower adherence to AI-enabled mental health tools [11].

AI’s role in clinical settings is to support and not replace human decision-making. Using electronically accessible information such as EHRs, wearable information, and patient-reported outcomes, AI can identify information that is invisible to clinicians, such as early signs of depressive relapse [28]. For instance, machine learning–based models for predicting antidepressant efficacy (AUC=0.76) allow for personalized selective serotonin reuptake inhibitor prescriptions; therefore, minimize trial and error delays [10]. Clinicians using AiCure, an AI platform used to track medication adherence using facial recognition, showed nearly 25% higher patient compliance in schizophrenia trials [31]. However, integration challenges remain; approximately 45% of therapists express distrust toward AI due to “black box” algorithms. Moreover, workflow disruptions often occur when AI recommendations conflict with clinical intuition [5]. Regulatory frameworks such as the Food and Drug Administration’s (FDA) SaMD (Software as a Medical Device) guidelines are equivalent to a standardization of AI validation, but nearly 15% of mental health applications meet evidence-based criteria [33]. Training programs, such as the American Psychological Association’s Digital Mental Health Certification, are upskilling clinicians to critically interpret output from AI so that they encourage collaboration, not competition [11].

AI is aiding patient agency by bringing complex information into actionable knowledge. For example, MindDoc uses AI to analyze journal entries and wearable data and provide personalized psychoeducation information on what causes depression [34]. Likewise, the neural network prediction model developed by Chekroud et al [10] allows patients to see antidepressant efficacy rates in comparison, which encourages collaborative decision-making between clinicians and their clients. Overreliance upon AI, however, may contribute to automation bias, wherein patients blindly support algorithmic recommendations. In a trial for Woebot, 25% of clients remanded important choices to the chatbot, in disregard for disclaimers about its scope [9]. To mitigate these concerns, tools such as IBM’s AI Explainability 360 provide accessible, layman-friendly explanations for AI-driven recommendations. For example, a system might clarify that a user’s sleep score decreased due to 3 specific events occurring after midnight during the past week, effectively bridging the gap between technical output and patient understanding [35].

Transparency is key to ethical AI deployment. Patients have to be informed about when AI is affecting their care, what types of data are being used (eg, social media and EHRs), and how errors are being handled. The guidelines issued by the FDA on SaMD require mental health applications to report accuracy rates and failure modes. For example, Wysa openly admits that its NLP model sometimes misclassifies some sarcasm as suicidal ideation in 12% of cases [29]. Clinicians who use AI tools such as Glimmer (a suicide risk predictor) are taught to put algorithmic risk scores in the context of qualitative patient narratives so that transparency does not undercut therapeutic trust [13]. However, black box models are dominant in the field of mental health AI, where a review showed that up to 15% of studies that used deep learning provide model interpretability metrics [36]. Emerging frameworks such as Local Interpretable Model-Agnostic Explanations (LIME) [37] and the European Union’s GDPR “right to explanation” would be pushing the field toward auditable AI, albeit with mixed compliance with the requirements in nonclinical applications [14].

Table 4 presents a comparative synthesis of studies on various AI modalities used in mental health diagnosis. The study summarizes key aspects such as AI modality, study design, diagnostic accuracy, and population. In addition to this, the study also incorporated pooled diagnostic accuracy ranges. This synthesis allows for a clearer understanding of the relative strength of evidence across the included studies.

The past studies show a strong trend in AI’s diagnostic capabilities across different modalities. NLP-based models show diagnostic accuracy ranging from around 81% to 89% for depression detection. Wearable sensors (eg, HRV) offer F₁-scores nearly from 0.70 to 0.81 for anxiety detection. In addition to this, the multimodal AI (combining text, wearables, and EHR data) offers the highest performance, with AUC ranging from 0.85 to 0.91, especially in predicting depression relapse and suicide risk.

AI technologies, for instance, NLP and wearables integrated with biometric sensors, have demonstrated remarkable promise in improving the early detection of mental health conditions. However, a critical appraisal of the evidence reveals a significant imbalance in study types. The NLP-based models trained on social media data have shown approximately 89% accuracy in detecting depression and an AUC of 0.92 for suicide risk detection as identified by Gkotsis et al [6] and Coppersmith et al [21]. While these results are descriptively rich, the strength of this evidence is tempered by its retrospective nature and the disproportionate representation of NLP studies in current literature, which often lack the prospective validation required for clinical diagnostic standards.

The wearable devices like Empatica E4 have shown F₁-score around 0.81 in predicting anxiety episodes in some cases [7]. These technologies are particularly beneficial in relation to scalability as well as cost-effectiveness. Further, they offer real-time tracking that has the potential to improve access to mental health care in resource-limited settings significantly. In addition, the advances in neuroimaging AI, including CNNs in schizophrenia diagnosis, have further reduced the importance of symptoms as reported by the patients [26].

Further, AI-enabled diagnostic tools consistently show high accuracy; nevertheless, when weighing these conclusions, it is vital to note that “high accuracy” in a controlled dataset does not always translate to real-world efficacy. However, there are still challenges such as the cultural bias in the NLP models. For instance, models trained on Western data often predict poorly for any non-English data as explained by Harrigian et al [22]. Additionally, wearables have problems such as sensor noise and adherence, which are major challenges in terms of real-world applications [11]. Therefore, while the volume of diagnostic evidence is high, the “certainty” of its impact in diverse clinical settings remains moderate due to these generalizability gaps. Furthermore, multimodal AI systems that combine EHRs with wearable data hold promise but require interoperability standards to function seamlessly, as highlighted by Tseng et al [28].

AI-driven interventions such as Woebot and MindDoc provide access to personalized feedback and self-management tools, which are available 24/7. In contrast to the high volume but often retrospective NLP research, therapeutic AI research, though smaller in number of studies, often uses rigorous methods such as randomized controlled trials. These tools have demonstrated major promise with Woebot, showing an approximate 30% reduction in anxiety symptoms in clinical trials as claimed by Darcy et al [9] and Brancati et al [34].

This stratification of evidence suggests that although we have more data on AI’s potential for diagnosis (NLP), the evidence of AI’s potential for therapeutic effectiveness (chatbots) is often backed by higher-quality, prospective evidence. These interventions give the power to patients and are evolving personalized and scalable treatment beyond the clinic hours. AI chatbots like Woebot have proven to be effective when it comes to personalizing care and offering more engagement to the patients, especially those who live in urban and rural spaces. However, one important limitation is the lack of empathy displayed by such AI systems (something required in cases of high risk, such as suicidal thoughts or episodes of severe depression). Additionally, there can be hazards of algorithmic reliance, as about 25% of the patients reported their not being able to trust AI for decision-making [9].

Arya et al [35] and WHO [42] studied XAI methods such as LIME and SHAP to address this issue by providing an explanation for AI results. However, it has been reported that 40% of lower poverty clients find explanations using technical descriptions difficult [43]. Ultimately, the relative strength of evidence for therapeutic augmentation is high in terms of methodology but limited by smaller sample sizes compared to the massive datasets that are used in NLP-based screening.

AI tools have the potential to enhance patient autonomy greatly by giving real-time feedback, psychoeducation, and self-management tools such as MindDoc and Woebot. These interventions are essential for reducing stigma and increasing engagement by providing private access support. However, as the AI involvement in patient decision-making increases, questions about the overreliance and algorithmic paternalism also emerge. AI has proven useful in empowering patients by providing them with tools for self-management of their own mental health. However, there is a growing dependence on algorithms, and nearly 25% of patients trust the decisions that AI makes over those that clinicians make [9]. The digital literacy gap is still a big challenge. Many patients, especially those who are from lower socioeconomic strata or from the aged population, may have difficulty properly engaging with the tools with AI integration. Additionally, AI-enabled interventions without human empathy may not be as effective in building actual patient engagement. There are dangers of biases in AI, as models misdiagnose marginalized groups, for example, Black people forming words [36]. In addition, Rocher et al [40] warn that sensitive data, for example, social media and biometric inputs, to be at risk of reidentification, raising privacy concerns.

As AI continues to be integrated into mental health care, the issue of legal accountability is a key one. Frameworks, such as GDPR and HIPAA, are making key guidelines for patient privacy. However, there are, however, concerns regarding compliance and accountability, where around 45% of mental health applications do not have HIPAA-compliant encryption [33]. There is a resulting growing consensus about the need for strong regulatory frameworks for ensuring that AI systems comply with privacy standards. However, the lack of clear definitions about liability in case of mistakes or misdiagnosis by using AI is still a major concern. AI models are often described as “black boxes”; thus, it is difficult to assign liability for errors. Moreover, no audits to verify bias and XAI tools in many systems bring questions into the topic of algorithmic fairness and transparency [48].

AI’s uses also require clear guidelines in the ethical standards aspect as a result of the problems regarding openness, reduction of biases, and involvement of multidisciplinary teams. The problem of a lack of accountability frameworks remains [5], but approaches such as federated learning [38] and differential privacy [41] promise ways of solving the data privacy and fairness problem in algorithms. The ethical framework for these systems should include mandatory testing for bias, transparency requirements on XAI, as well as compliance with privacy regulations on the order of GDPR and HIPAA.

Studies such as Jobin et al [14] and Nwosu et al [31] were concerned about the centrality of accountability for clinical AI applications. A few other studies had some measure to improve medication adherence with the use of AI tools such as AiCure [31], despite the general skepticism from therapists about “black-box” AI models [5]. A study of Kandeel et al [50] highlights the importance of interdisciplinary collaboration between clinicians, AI developers, and ethicists to ensure that these systems uphold patient autonomy, equity, and ethical standards. In the larger context of the regulatory frameworks, Afify et al [3] highlighted the role of environmental law, underlining the importance of the legal provisions in managing new emerging challenges in technology. The study was focused on AI in mental health. The study highlights the importance of adequate legislation to reduce the threat of new environmental change, which is similar to providing accountability and fairness in the application of AI in health care. Similarly, Mohamed [46] outlined the legal consequences and accountability of fully AI-based surgeries. The summation of the paper under review about the application of AI in mental health care is further represented in Table 5.

This review has a few limitations, which should be taken carefully into consideration while interpreting the overall study findings. First, in this study, the use of only English-language papers was considered. While this is a common approach in systematic reviews, this perhaps may have led to the exclusion of relevant studies if the paper was written in other languages. This will bring potential bias in language and will restrict the global representativeness of the evidence base (especially from non-English languages).

Second, there is a great heterogeneity across the included studies. The studies encompass several variations in study designs such as different uses of AI techniques, applications for mental health conditions, outcome measures, and considered evaluation frameworks. Due to this heterogeneity, it was not possible to carry out a meta-analytical synthesis. This review presents a broad overview; thus, the limited results of pooled statistical analysis may restrict the provision of definitive conclusions on the effective use of AI in mental health in various contexts. Consequently, the findings should be interpreted as a qualitative and thematic synthesis and not as conclusive evidence of efficacy.

Third, the included studies reported diverse and nonstandardized outcomes; therefore, comparability across studies was a major constraint. This heterogeneity in outcome definitions and ways of measurement may have impacted the consistency and generality of the conclusions drawn on issues of diagnostic accuracy, treatment effectiveness, and patient empowerment.

In addition, there was no registration of formal protocols (eg, PROSPERO) included in the review. Besides, the set systematic review guidelines were followed fully. The lack of previous registration could lead to a lower degree of transparency in a priori methodological choices and a less comprehensive ability to formally evaluate possible deviations from the planned review process.

Finally, there are legal, ethical, and regulatory interpretations of AI in mental health that also vary considerably across different regions. As a result, the findings relating to privacy, data protection, and regulatory compliance, particularly those which refer to frameworks such as GDPR and emerging AI regulations, may not be equally applicable in all legal contexts. This jurisdictional variability is a limitation in directly transferring legal and policy-related conclusions.

Despite these limitations, this review offers a structured and comprehensive synthesis of the available literature and identifies key research gaps, methodological challenges, and regulatory considerations that can inform future empirical research and policy development in the context of AI-enabled mental health care.

The study, after reviewing several papers, offers the following recommendations:

Overall, based upon a critical and systematic review, comparison, and coherence of studies, this study develops a review of findings as illustrated in Figure 2.

The widespread adoption of AI in mental health represents a significant technological evolution. Besides, its role as a paradigm shift remains a future potential rather than a current clinical reality. In addressing mental health care needs, AI demonstrates potential as a supportive tool for screening, monitoring, and digitally delivered interventions. Nonetheless, current evidence remains heterogeneous and context-dependent, with the strength of conclusions often limited by the varying risk of bias across included studies. Claims regarding cost-effectiveness, cultural sensitivity, and large-scale clinical impact require further empirical validation through robust, longitudinal, and comparative studies that prioritize high-certainty evidence. AI-enabled devices, such as NLP models, wearables equipped with biometric sensors, and neuroimaging procedures, have been found to be useful in the diagnosis of mental health disorders such as depression, anxiety, and schizophrenia. NLP models from social media, for example, have been found to be extremely accurate at the detection of suicide and depression, although the results are highly underrepresented elsewhere in terms of clinical trials and are instead collected retrospectively. Wearables such as Expatica’s E4 have been found helpful for real-time physiology monitoring.

As these technological advancements are significant, the weighting of their clinical impact must be balanced against the prevalence of moderate-to-high risk of bias in the current literature. The use of AI for the treatment of mental health has high ethical, technical, and practical implications. Data privacy concerns about algorithmic bias and the need for culturally sensitive devices bring home the difficulty of integration of AI in clinical practice. Use-based dependency upon AI devices has raised the problem of patient agency and algorithmic dependency, particularly if a significant judgment is being left in the hands of chatbots. These concerns should be addressed through adequate ethical guidelines, stratification of evidence courtesy of its real-world applicability, involvement of stakeholders, and constant evaluation of AI technology for the greatest potential of AI in mental health treatment. All these will allow AI as an aid for human expertise and not as a substitute for human expertise, while ensuring patient confidence and safety, and improving the quality and access of mental health services. Additionally, AI has the potential to revolutionize mental health care, though the necessary regulatory frameworks, such as GDPR and HIPAA, have to have a complete integration with the development and deployment of these systems. For AI to be effective and ethical in the treatment of mental health, a continued effort of research and development involves the importance of a rigorous analytical framework, regulatory compliance, transparency, and patient autonomy. By addressing these challenges and focusing on high-quality, prospectively validated evidence, AI can be implemented in a manner that ensures data privacy, reduces algorithmic bias, and promotes trust between patients and health care providers.

The future research directions are as follows: