In this systematic review, we followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (Multimedia Appendix 1). The review was started in February 2025 using the 5 main databases, such as PubMed, Google Scholar, IEEE Xplore, ScienceDirect, and Scopus, following specific search queries (provided in Multimedia Appendix 2). Only peer-reviewed publications published between January 2020 and February 2025 were included in the search parameters. To maintain methodological integrity, we have only included original research articles that were written in English language. In this review, book chapters, structured or unstructured reviews, observational notes, and position papers were not included.

The search string (Multimedia Appendix 2) was carefully designed to fit the emphasis of our work. Interventions of our interest included the use of smart devices and AI-enabled or precision medicine approaches for mental health. Moreover, we considered research indicating promise for personalized treatment. The study excluded studies that are not connected to mental health treatment or AI-assisted solutions.

One team member (SU) performed the initial screening of studies by title. Then, 2 individual reviewers (SU) and (CR) were given the task of carrying out the screening, determining eligibility, and managing the inclusion procedure. After careful consideration, any discrepancies between the 2 teams were fixed through agreement. Both reviewers independently then screened the potential studies by title and abstract. A third team (IS) was consulted to make the final decision in cases where an agreement could not be reached. From February to March 20, 2025, the screening procedure was carried out following the inclusion and exclusion criteria. Studies that did not relate to AI-driven personalized interventions for mental health treatment were not considered. Additionally, articles that focused on other medical conditions, were not subject to peer review, or were editorials, interviews, or position papers, were not included.

The PRISMA flow diagram summarizes the study selection process. The initial search yielded a total of 76 citations, from which 13 duplicates were removed. The remaining records were screened based on their titles and abstracts, leading to the exclusion of 15 citations that did not meet the inclusion criteria. The remaining 48 studies were then subjected to full-text review, during which 3 papers were excluded. The primary reasons for exclusion included the use of ineligible manuscript types such as published abstracts, studies that focused on mental health treatment without addressing personalized care or precision medicine, and papers that did not involve the application of machine learning or AI. After the screening process, a total of 28 studies were included in the final review.

For eligible studies, data were extracted from the studies by 2 teams (IS and CR), and the whole process was reviewed by a third team (SU). Data were extracted from the potential studies by following the RQs. First, information about data sources, data collection methods, data types, preprocessing techniques, and AI models used in the studies was extracted from the reviewed studies. Second, both teams (IS and CR) gathered information about AI methodologies and approaches, real-time monitoring scopes, personalization strategies, intervention models, critical influencing factors, challenges in AI implementations, target population and user groups, and associated mental health conditions. Third, insights were gathered on AI explanation methods (including explainability and interpretability techniques), system design and implementation challenges, trade-offs between model performance and interpretability, clinician-centered barriers and real-world integrations, as well as the impact of AI on clinical decision-making and patient outcomes. After every step, the analysis was evaluated and fine-tuned by another team (SU).

The team members of this research analyzed the findings of the review. During our analysis, we emphasized more on real time monitoring tools, personalized approaches, and AI models used. This thorough analysis helped us to develop our comprehensive conceptual framework.

We analyzed the included studies (Figure 3) and organized the core findings based on RQs. The findings for our RQ1, RQ2, and RQ3 can be found in Multimedia Appendices 3-5 [22-49], consequently. In addition, the shortcomings of single approaches and what complementary methods can be adopted in this case are shown in Table 1.

Building upon the findings from this systematic review, we identified persistent challenges within the realm of AI-driven mental health interventions. Specifically, the lack of adequate personalization, minimal explainability, and insufficient integration into established clinical workflows. These deficiencies are particularly significant in high-burden health care domains, such as AD/ADRD, where caregivers contend with both emotional distress and intricate informational demands. The insights we gathered from analyzing different studies demonstrated the importance of developing a more adaptive, transparent, and contextually aware AI framework. In the subsequent sections, we have used our findings to propose a dynamic personalization and explainable decision-making enabled human-AI collaborative system for caregivers of individuals with ADRD.

In this review, we analyzed 28 papers on personalized AI-powered mental health treatment strategies by examining the types of data used to detect early warning signs of mental disorders. We also explored how AI-enabled systems deliver real-time monitoring and dynamically personalize treatment plans based on individual user behavior and engagement patterns. Moreover, we identified the challenges associated with designing and validating explainable AI models. We also discussed the limitations of the reviewed studies following our RQs, to identify key gaps and opportunities for future work, as outlined below.

When designing personalized mental health intervention tools, there is no “one-size-fits-all” solution, particularly when it comes to selecting and tailoring input data sources [72]. In our review, it was visible that different studies used different approaches. In our review, though we found a large number of studies used self-reported questionnaires to get information about mood and symptoms [31,34,35,38,39,48], there are some major issues associated with this type of input. For instance, Stone et al [73] highlighted some of the major issues in explaining EMA in behavioral medicine. According to the author, a major issue with traditional self-reported questionnaires is recall bias, which means that people might forget, underreport, or misrepresent how they felt or what symptoms they had in the past. Another issue is social desirability bias, which happens when people change their answers to make them seem more acceptable or favorable [74]. Furthermore, some studies used NLP-derived textual features [31,32,42] to include detailed insights. However, these studies might face issues including contextual ambiguity [75], domain adaptation [76], and ethical concerns related to privacy and explainability [77,78]. The key limitations of the key data types are shown together in Figure 4.

In addition to these choices for collecting data, our review also found that reporting practices were not very clear or consistent. There was a significant gap with enough details about the steps for preprocessing and integrating data. For example, only a few of the 6 (21.4%) studies that used surveys and interviews [31,34,35,38,39,48] clearly explained how these were processed or combined with other types of data. These gaps make it hard to see important methodological choices about data cleaning, normalization, and integration that can have a significant impact on AI performance [79,80]. Last, we found that methodological integration and performance evaluation were underexplored. For instance, only 2 studies [40,41] looked at how to combine different types of data, like clinical records with smartphone-based sensing or wearable-derived physiological signals. In the same way, only 2 (7.1%) studies [23,29] looked at model performance indicators like accuracy and prediction.

In the last several years, mental health systems have been increasingly moving toward personalization based on the idea that no 2 people deal with mental health issues in the same manner [72]. In personalization, real-time monitoring is one of the crucial variables. Traditional static AI mental health care approaches rely on retrospective or infrequent assessments that fail to capture dynamic, context-dependent symptom changes. Real-time monitoring solves this problem by gathering continuous, real-world data on mood and behavior [81]. Therefore, real-time monitoring tools enable digital mental health technologies that are aware of the environment to provide more flexible and effective treatment [78]. However, in our review, we found that even though real-time monitoring tools are essential, they were not widely used or used consistently in our studies. Instead, many studies used static or scheduled assessments that are incapable of detecting quick mood changes or environmental triggers. For example, studies [33,34] used retrospective mood questionnaires that were given weekly or at clinic visits, which are unable to inform timely, personalized interventions.

In our review, different personalization methods were found in mental health AI interventions, such as mood-based personalization [35,39,42,43], clinical stratification or risk profiling [34,40,41,45], and general personalization frameworks without dynamic adaptation [24,48]. However, all these personalization methods have some drawbacks. For instance, mood-based personalization often uses self-reported ratings from a single source that fails to consider evolving, contextual, or physiological factors that are important for adaptive mental health support [82,83]. In addition, clinical stratification and risk profiling often use static baseline assessments that disregard considering behavior or physiology, which makes adaptive prediction less accurate [84,85]. General personalization frameworks, on the other hand, use fixed rules that cannot change when users do, which makes them less relevant and engaging [86]. These limitations indicate significant gaps where multimodal data integration might be beneficial. Multimodal data integration can provide a comprehensive view of mental health states by combining different types of data, like physiological signals from wearables, self-reported mood ratings, and text written by users.

Explainability is one of the main ingredients of personalization because it helps users understand and trust AI-driven decisions. For instance, in the study by Kaur et al [87], participants used a bird identification system with 4 different explainable AI methods, and reported that explanations helped them calibrate their trust in the AI’s output and improve their own identification skills. This finding illustrates how meaningful explanations can increase user confidence and support effective use of AI systems, a principle that is equally important in sensitive contexts like mental health care. In our review, we found that while many studies used tree-based and classical models for inherent interpretability [22,26,35,43,45,46], others used deep learning interpretability [29,49], NLP-based tools [30,42], feature selection [27,34], model evaluation visualizations [38,39], feature importance tools [33,37], hybrid methods [41,44,48], and naive Bayes approaches [40]. However, several studies did not report any explainability approach at all [23-25,28,31,32,36,47]. Therefore, this highlights the ongoing need for more consistent and thoughtful use of explainability techniques in mental health AI systems.

Our review revealed that few studies adequately addressed the issue of generalizability. The study by Webb et al [45] specifically highlighted that models trained on limited or homogeneous data may demonstrate suboptimal performance across varied demographic, cultural, or clinical contexts, thereby emphasizing the risk of inequitable outcomes in real-world mental health care. Besides, since this limitation is rooted in the diversity of people whose data is used, inputs such as wearables, mood-based self-reports, and text data alone do not inherently guarantee broader generalizability [88]. At the same time, another critical challenge in developing AI for mental health is balancing predictive performance with interpretability. Several studies in our analysis chose high-performing but black-box models [33,34] without sufficiently addressing how their lack of transparency may undermine clinician confidence or safe implementation. Another issue that is quite common in AI interventions related to mental health is the trade-off between model accuracy and practical usability. For instance, a limited number of studies [22,35,38] clearly addressed the trade-off between model accuracy and practical usability, underscoring a deficiency in design thinking crucial for real-world implementation. However, users often choose systems that are somewhat less accurate but more understandable, since they enhance comprehension, trust calibration, and informed decision-making [87]. Therefore, carefully addressing this trade-off is essential to guarantee that AI systems provide clinically significant help while maintaining transparency and acceptability for both practitioners and patients.

From the findings of the review, we found that the existing personalized mental health–related AI approaches have significant shortcomings in terms of explainability, transparency, and usability testing. In that case, our proposed framework directly addresses these deficiencies by furnishing a clear, user‑specific rationale for every recommendation—for example, a prompt suggesting a 20‑minute walk is justified by noting that the caregiver previously reported this activity as effective during periods of elevated stress. By consistently articulating the reasoning behind each action, the system enhances transparency, cultivates user trust, and supports sustained engagement.

The development of this conceptual framework presents profound opportunities for future work. Further research might incorporate the technology acceptance model to evaluate the adoption and usability of the proposed system. This will help to understand how users will accept and use this technology, focusing on 2 primary constructs such as perceived usefulness and perceived ease of use. Besides, according to Kushniruk and Patel [90], usability testing enhances generalizability by uncovering obstacles and design deficiencies across many user groups and circumstances, which will overcome the limitations found in the review. Therefore, deploying the framework in real-world environments will allow us to assess its scalability, integration with institutional routines, and effectiveness in supporting professional caregivers alongside family members.

This study has several notable limitations. First, none of the included studies were about patients with AD/ADRD or targeted caregivers. The studies were more focused on AI-driven personalized mental health care among the general population. However, this also serves as one of the strengths of this study. By analyzing the global advancement in personalized AI mental health interventions, we will be able to apply the outcome of the extracted foundational principles, such as mood-based personalization and explainability, from the review. This allowed us to design a forward-looking framework that can be adapted and validated in future contexts specific to caregivers of individuals with AD/ADRD.

Second, the proposed framework has yet to be evaluated through broader lenses with external experts. Third, there is a profound need for real-world usability studies to assess how caregivers interact with the system, perceive its usefulness, and engage with its adaptive and emotional support features. This has been reserved as one of the future works that will help the system to be fine-tuned.

Fourth, despite the review focused on studies that implemented AI-based personalized mental health interventions, we also included several papers in which the authors proposed that their models could potentially support personalization in future applications. This also worked as a strength of this study, as this enabled us to capture emerging perspectives and early-stage innovations, which kept us updated about the future landscape of AI-driven mental health care.

Fifth, the framework is currently suitable for home-based settings. However, we aim to facilitate the framework in a facility-based care setting and incorporate it with clinical experts. Finally, the review was done with studies that were published between January 2020 and February 2025; yet, new studies are continuing to be published. Therefore, an updated review of AI-driven mental health care interventions may be warranted in the near future.

Despite having these limitations, our proposed framework, especially on personalized mental health research on caregivers of patients with AD/ADRD, serves as a technically robust and future-proof design. However, future work is needed to check user acceptance and tailor these findings through caregiver-specific research.