Mental health has become a global public health priority, with increasing recognition of its importance for individual well-being, societal stability, and economic productivity. According to the World Health Organization, approximately 1 in 8 people worldwide live with a mental health disorder [1]. Despite the growing demand for mental health services, traditional approaches such as in-person therapy and medication, which rely heavily on trained professionals and extensive infrastructure, are struggling to meet the rising need [2]. Consequently, an alarming 76% to 85% of individuals with mental health disorders do not receive effective treatment, often due to barriers such as limited access to mental health professionals, social stigma, and inadequate health care systems [3]. Against this backdrop, advances in generative artificial intelligence (GenAI) offer new and promising avenues to enhance mental health services.

GenAI, such as ChatGPT [4], is built on large-scale language modeling and trained on extensive textual corpora. Their capacity to produce contextually relevant and, in many cases, emotionally appropriate language [5,6] enables more natural and adaptive interactions. Compared to earlier dialogue systems, GenAI exhibits greater flexibility in producing open-ended, humanlike dialogue [7]. This generative capability makes them a promising tool for web-based therapeutic interventions that allow for real-time, adaptive engagement in mental health care.

Currently, GenAI is being integrated into mental health through a range of innovative applications. For instance, GPT-driven chatbots such as Well-Mind ChatGPT [8] and MindShift [9] provide personalized mental health support by engaging users in conversational therapy. Similarly, virtual companions such as Replika [10] are used to help users manage feelings of loneliness and anxiety through interactive dialogue [11]. In addition, GenAI has been used to analyze social media posts and clinical data to identify signs of depression [2] and suicidal ideation [12]. These diverse applications illustrate the potential of GenAI to address various mental health needs, from prevention and assessment to continuous support and intervention.

Although research has investigated various applications of GenAI in mental health, much of it has focused on specific models or isolated cases, lacking a comprehensive evaluation of its broader impacts, applications, and associated risks. Similarly, most systematic reviews to date have focused on particular domains, such as depression detection [13], chatbot interventions [14], empathic communication [5], psychiatric education [15], and AI-based art therapy [16]. While such focused reviews offer valuable insights into specific use cases, a broad outline remains crucial for understanding overarching trends, identifying research gaps, and informing the responsible development of GenAI in mental health. To date, only 2 reviews [17,18] have attempted broader overviews, covering the literature published before April 2024 and July 2023, respectively. However, since April 2024, the rapid evolution of GenAI—including the release and deployment of more advanced models, such as GPT-4o [19] and GPT-o1 [20], and their increasing integration with clinical workflows, such as Med-Gemini [21], has expanded the scope and complexity of GenAI applications in real-world mental health contexts. These developments underscore the need for a more updated and integrative synthesis.

To address this gap, we aimed to provide a comprehensive overview of GenAI applications in mental health, identify research gaps, and propose future directions. To systematically categorize the existing research, we divided the studies into three distinct categories based on the role of GenAI in mental health applications, as illustrated in Figure 1: (1) GenAI for mental health diagnosis and assessment, encompassing research that leverages GenAI to detect, classify, or evaluate mental health conditions; (2) GenAI as therapeutic tools, covering studies where GenAI-based chatbots or conversational agents are used to deliver mental health support, therapy, or interventions directly to users; and (3) GenAI for supporting clinicians and mental health professionals, including research aimed at using GenAI to assist clinicians in their practice.

Despite these promising applications, the integration of GenAI into mental health care is not without challenges. Applying GenAI in the mental health field involves processing highly sensitive personal information, such as users’ emotional states, psychological histories, and behavioral patterns. Mishandling such data not only poses privacy risks but may also lead to psychological harm, including distress, stigma, or reduced trust in mental health services [22]. Therefore, in addition to systematically categorizing existing applications of GenAI in mental health, we also examined ethical issues related to their use in this domain. On the basis of our analysis, we proposed an ethical framework, GenAI4MH, to guide the responsible use of GenAI in mental health contexts (Figure 2).

We conducted this systematic review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines (Multimedia Appendix 1) [23]. We conducted a comprehensive search across 6 databases: PubMed, ACM Digital Library, Scopus, Embase, PsycInfo, and Google Scholar. We conducted the search between October 1, 2024, and October 7, 2024, and targeted studies published from October 1, 2019, to September 30, 2024. The starting date was chosen to coincide with the introduction of the T5 model [24], a foundational development for many of today’s mainstream GenAI models. This date also intentionally excluded earlier models, such as Bidirectional Encoder Representations from Transformers (BERT) [25] and GPT-2 [26], as these models have already been extensively covered in the previous literature [27,28], and our aim was to highlight more recent innovations.

Search terms were constructed using a logical combination of keywords related to GenAI and mental health: (Generative AI OR Large Language Model OR ChatGPT) AND (mental health OR mental disorder OR depression OR anxiety). This search string was developed based on previous reviews and refined through iterative testing to ensure effective identification of relevant studies. When possible, the search was restricted to titles and abstracts. For Google Scholar, the first 10 pages of results were screened for relevance. A detailed search strategy is provided in Multimedia Appendix 2.

The selection criteria included studies that (1) used GenAI and were published after the introduction of the T5 [24] model and (2) directly addressed the application of GenAI in mental health care settings. Only peer-reviewed original research articles were considered, with no language restrictions.

Data from the included studies were extracted using standardized frameworks. For qualitative studies, we used the Sample, Phenomenon of Interest, Design, Evaluation, and Research Type (SPIDER) framework. For quantitative studies, we applied the Population, Intervention, Comparison, Outcome, and Study (PICOS) framework. A summary of the extracted data are provided in Multimedia Appendix 3 [2,3,7-9,11,12,29-100].

To assess the reporting transparency and the methodological rigor of the included studies, we applied the Minimum Information about Clinical Artificial Intelligence for Generative Modeling Research (MI-CLAIM-GEN) checklist (Multimedia Appendix 4) [101], a recently proposed guideline tailored for evaluating the reporting quality of research on GenAI in health care. The checklist covers essential aspects such as study design, data and resource transparency, model evaluation strategies, bias and harm assessments, and reproducibility. We followed the Joanna Briggs Institute quality appraisal format [102] to score each item in the checklist using 4 categories: yes, no, unclear, and not applicable.

As shown in Figure 3, a total of 783 records were initially retrieved from the 6 databases. After removing duplicates, 73.8% (578/783) of unique records remained for screening. Following abstract screening, 39.4% (228/578) of the records were identified for full-text retrieval and screening. After full-text screening, 24% (55/228) of the articles were selected for inclusion in the systematic review. To ensure comprehensive coverage of relevant studies, a snowballing technique was then applied, where we examined the reference lists of the included studies and related review articles. This process identified an additional 44 studies for eligibility assessment. After the same evaluation process, 54% (24/44) of these studies met the inclusion criteria, bringing the final total to 79 studies for the systematic review. Two PhD candidates (YZ and XW) independently conducted the selection, with discrepancies resolved through discussion. The interrater reliability was satisfactory (κ=0.904).

An analysis of publication trends over time reveals a growing focus on the application of GenAI in mental health (Figure 4). Overall, the number of studies in all the 3 categories grew extensively over the examined period, indicating a rising interest in using GenAI for mental health. In 2022, the total number of studies was minimal across all the 3 categories, with only 1 (1%) early study, of the included 79 studies, emerging on the use of GenAI for mental health diagnosis and assessment. However, as GenAI advanced and garnered wider adoption, the number of publications in all the 3 categories began to increase steadily. A moderate increase was observed in the year 2023, with 13% (10/79) of the studies focused on diagnosis and assessment, 9% (7/79) on therapeutic interventions, and 8% (6/79) on clinician support, reflecting a growing interest in practical applications of these models in health care settings. By 2024, the number of publications had surged across all the 3 categories, with 33% (26/79) of the studies focused on diagnosis and assessment, 16% (13/79) on therapeutic interventions, and 23% (18/79) on clinician support.

Of the 79 included studies, 20 (25%) investigated the use of GenAI-based chatbots and conversational agents to facilitate interventions ranging from emotional support to more structured therapies. To assess the feasibility and potential impact of these interventions, we analyzed studies across four key dimensions: (1) therapeutic targets, (2) implementation strategies, (3) evaluation outcomes, and (4) real-world deployment features.

Of the 79 included studies, 24 (30%) focused on applying GenAI to support clinicians and mental health professionals, with 2 (2%) overlapping with the research on GenAI models for mental health diagnosis and assessment.

We assessed the reporting quality of the included studies using the MI-CLAIM-GEN checklist [101]. Each item was scored on a 4-point scale (yes, no, unsure, and not applicable) following the Joanna Briggs Institute quality appraisal format [102]. The results are presented in Figure 7.

On average, 45.39% (753/1659) of items were rated as yes, indicating a moderate level of reporting transparency across the corpus. Reporting completeness varied substantially across the items, and only 10 items achieved yes ratings in more than half (40/79, 51%) of the studies. As shown in Figure 7, items related to study design (items 1.1–1.5), model performance and evaluation (items 3.1–3.4), and model examination (items 4.1–4.5) were most consistently reported, with 73.9% (292/395), 56% (177/316), and 54.1% (171/316) of the studies achieving yes ratings, respectively. In contrast, items concerning resources and optimization (items 2.1–2.4) and reproducibility (items 5.1–5.3) were frequently underreported, with 25.3% (100/395) and 5.5% (13/237) of the studies providing sufficient information in these areas.

Item-level analysis further revealed critical disparities. Core design elements were consistently addressed—for instance, 1.1 (study context) and 1.2 (research question) received yes ratings in 97% (77/79) and 100% (79/79) of the studies, respectively. However, items, such as 1.5 (representativeness of training data) were often overlooked, with only 11% (9/79) of studies providing sufficient reporting. Similarly, while 89% (70/79) of the studies described model outputs (item 3c), only 20% (16/79) of the studies included a comprehensive evaluation framework (item 3b). Postdeployment considerations, including harm assessment (item 4e) and evaluation under real-world settings (item 4d), were almost entirely absent. In the reproducibility domain, none of the studies provided a model card (item 5b), and only 14% (11/79) of the studies reached tier-1 reproducibility by reporting sufficient implementation details (item 5a).

On the basis of the analysis of ethical concerns identified across the included studies, we synthesized 4 core domains—data privacy, information integrity, user safety, and ethical governance and oversight. Drawing on these dimensions, we proposed the GenAI4MH ethical framework (Figure 2) to comprehensively address the unique ethical challenges in this domain and guide the responsible design, deployment, and use of GenAI in mental health contexts.

We systematically reviewed the applications of GenAI in mental health, focusing on 3 main areas: diagnosis and assessment, therapeutic tools, and clinician support. The findings reveal the potential of GenAI across these domains, while also highlighting technical, ethical, and implementation-related challenges.

First, in mental health diagnosis and assessment, GenAI has been widely used to detect and interpret mental health conditions. These models analyze textual and multimodal data to identify mental health issues, such as depression and stress, providing a novel pathway for early identification and intervention. Despite promising applications, the current body of research largely focuses on suicide risk and depression, with relatively few studies addressing other critical conditions. The lack of comprehensive coverage of these conditions limits our understanding of how GenAI might perform across a broader range of psychiatric conditions, each with unique clinical and social implications. Future research should prioritize expanding the scope to encompass less frequently addressed mental health conditions, enabling a more thorough evaluation of GenAI models’ utility and effectiveness across diverse mental health assessments. Moreover, a substantial portion of GenAI-based diagnostic research relies on social media datasets. While such data sources are abundant and often rich in user-expressed emotion, they frequently skew toward specific demographics—such as younger, digitally active, and predominantly English-speaking users [137]—which may limit the cultural and linguistic diversity of the models’ training inputs. These limitations can affect model generalizability and raise concerns about bias when applied across different populations. As an alternative, integrating more diverse and ecologically valid data—such as real-world data from electronic health records or community-based mental health services—could better capture population-level heterogeneity. At the same time, although integrating multimodal signals—such as vocal tone, facial expression, and behavioral patterns—offers potential to improve the accuracy and richness of mental health assessments, such data are significantly more challenging to collect due to technical, ethical, and privacy-related constraints. Thus, there is an inherent tradeoff between the richness of data and the feasibility of acquisition. Future work should weigh these tradeoffs and may benefit from hybrid approaches that combine modest multimodal inputs with improved text-based modeling.

Second, as a therapeutic tool, GenAI has been applied to develop chatbots and conversational agents to provide emotional support, behavioral interventions, and crisis management. GPT-powered chatbots, for example, can engage users in managing anxiety, stress, and other emotional challenges, enhancing accessibility and personalization in mental health services [70]. By offering accessible and anonymous mental health support, these GenAI models help bridge gaps in traditional mental health services, especially in areas with limited resources or high social stigma, thus supporting personalized mental health management and extending access to those who might otherwise avoid seeking help. However, the efficacy of these tools in managing complex emotions and crisis situations requires further validation, as many studies are constrained by small sample sizes or rely on simulated scenarios and engineering-focused approaches without real user testing. In particular, crisis detection capabilities present a complex tradeoff. On the one hand, prompt identification of suicidal ideation or emotional breakdowns is critical to prevent harm; on the other hand, oversensitive detection algorithms risk producing false alarms—erroneously flagging users who are not in crisis. Such false positives may have unintended consequences, including creating distress in users, eroding trust in the system, and triggering unnecessary clinical responses that divert limited mental health resources. Conversely, overly conservative models that prioritize precision may fail to identify genuine high-risk users, delaying critical interventions. Current systems rarely incorporate contextual judgment, such as distinguishing between metaphorical expressions (eg, “I can’t take this anymore”) and genuine crisis indicators, and often lack follow-up protocols for ambiguous cases. Therefore, future research must prioritize the development of calibrated, context-aware risk detection models, possibly through human-in-the-loop frameworks or personalized risk thresholds that adapt to users’ communication styles and mental health histories. Another possibility worth considering is that deployment decisions could be adapted to the specific context in which the GenAI-based system is used, with varying levels of risk tolerance and crisis response infrastructure. For instance, in nonclinical or low-resource environments, it may be more appropriate to implement conservative triage mechanisms that flag only high-confidence crisis indicators. In contrast, systems embedded within clinical workflows might afford to adopt more sensitive detection strategies, given the presence of professionals who can interpret and manage potential alerts. Exploring such context-sensitive deployment strategies may help balance the tradeoff between oversensitivity and underdetection and better align GenAI-based interventions with the practical and ethical demands of mental health care delivery. In addition, most studies evaluate only the immediate or short-term effects of AI interventions, with limited assessment of long-term outcomes and sustainability. Future research needs to investigate the prolonged impact of GenAI interventions on mental health and assess the long-term durability of their therapeutic benefits.

Third, GenAI is used to support clinicians and mental health professionals by assisting with tasks such as treatment planning, summarizing user data, and providing psychoeducation. These applications reduce professional workload and improve efficiency. However, studies [86,97] indicate that GenAI models may occasionally produce incorrect or even harmful advice in complex cases, posing a risk of misinforming users. Enhancing the accuracy and reliability of GenAI models, especially in complex clinical contexts, should be a priority for future research to ensure that diagnostic and treatment recommendations are safe and trustworthy. Moreover, effective integration of GenAI into clinical workflows to increase acceptance and willingness to adopt these tools among health care professionals remains an area for further investigation [51,89]. Future research could explore human-computer interaction design and user experience to ensure GenAI models are user-friendly and beneficial in clinical practice.

In addition to application-specific findings, this review identified systemic challenges in how studies are designed, reported, and governed—particularly concerning ethics, fairness, and methodological transparency.

Ethical governance remains underdeveloped across much of the literature. Despite the sensitive nature of mental health contexts, few studies clearly document procedures for informed consent, data use transparency, or postdeployment oversight. Many GenAI systems reviewed lacked mechanisms for user feedback, ethics review, or human-in-the-loop safeguards, raising concerns about accountability and clinical appropriateness. Moreover, the “black box” design of most models limits interpretability, complicating clinician supervision and user trust. Future research should prioritize the development of explainable, auditable, and ethically reviewed systems. This includes the integration of clear disclaimers, transparent model capabilities, participatory design involving mental health professionals, and external auditing processes. Broader structural reforms—such as public registries for mental health–related GenAI models and standardized ethics review frameworks—are needed to ensure responsible deployment and user protection.

Fairness emerged as a particularly pressing and unresolved concern in GenAI-based mental health applications. Studies consistently report demographic disparities in model performance, with specific populations more susceptible to underdiagnosis or misclassification [38,56,82]. Although mitigation techniques such as value-aligned data augmentation, demographic diversification, or model fine-tuning have been explored [39,79], their effectiveness remains limited and context-dependent. Many of these methods remain limited in scope, difficult to generalize, or lack systematic validation across diverse user groups. Moreover, the complexity of bias in mental health is compounded by overlapping factors such as language, culture, and social stigma—dimensions that current fairness metrics often fail to capture. Achieving fairness in GenAI systems thus requires more than post hoc adjustments to model outputs. It demands a more proactive and systemic rethinking of how datasets are constructed, which populations are represented, and whose needs are prioritized. Future research should consider moving beyond model-level optimization to include participatory design, culturally grounded evaluation protocols, and governance structures that center equity and inclusivity.

Reporting quality also remains inconsistent. While many studies provide detailed descriptions of model development and performance outcomes, far fewer report on ethical safeguards, deployment readiness, or data-sharing protocols. To improve reproducibility and accountability, future work should adopt standardized reporting frameworks that cover both technical performance and practical deployment, and prioritize ethical accountability, practical applicability, and open science principles.

This review has several limitations. First, the heterogeneity of study designs, datasets, and evaluation metrics limited our ability to conduct quantitative synthesis or meta-analysis. Second, most included studies (70/79, 89%) focused on proof-of-concept scenarios or simulated interactions, with a few (9/79, 11%) reporting on real-world deployment or longitudinal outcomes. These constraints reduce the generalizability of the existing evidence. Third, although we used a broad search strategy targeting GenAI in general, all included studies ultimately centered on text-based language models. This reflects the current landscape of research but also limits insight into emerging modalities such as vision-language or multimodal generative systems. Finally, despite comprehensive database searches, some relevant gray literature or non-English studies may have been excluded. Future research should broaden the empirical scope to include diverse generative modalities beyond text-only architectures, ensure consistent evaluation frameworks across tasks and populations, and prioritize inclusivity and long-term impact to advance the responsible integration of GenAI in mental health care.

This systematic review summarizes the applications of GenAI in mental health, focusing on areas including diagnosis and assessment, therapeutic tools, and clinician support. Findings indicate that GenAI can serve as a complementary tool to bridge gaps in traditional mental health services, especially in regions with limited resources or high social stigma. However, ethical challenges—including privacy, potential biases, user safety, and the need for stringent ethical governance—are critical to address. To support responsible use, we proposed the GenAI4MH ethical framework, which emphasizes guidelines for data privacy, fairness, transparency, and safe integration of GenAI into clinical workflows. Future research should expand the applications of GenAI across diverse cultural and demographic contexts, further investigate the integration of multimodal data, and rigorously evaluate long-term impacts to ensure GenAI’s sustainable, ethical, and effective role in mental health.