The growing prevalence of anxiety disorders in the global population has driven the search for innovative technological solutions to improve their early detection and diagnosis. In this context, tools have emerged in recent years to analyze large volumes of data and detect complex patterns that may be indicative of psychological disorders (11). However, the diversity of available AI techniques and the methodologies used in their development raises questions about their effectiveness and applicability in different contexts. Therefore, the review carried out focuses on two fundamental questions: RQ1, which seeks to identify which AI techniques have shown better performance in the identification of anxiety disorders in diverse populations; and RQ2, which explores the methodologies and approaches used to train and validate these techniques. Addressing these questions will highlight best practices and possible areas for improvement in the recognition of anxiety disorders.

RQ1. What Artificial Intelligence techniques show the best performance in identifying anxiety disorders in diverse populations? RQ2. What methodologies and approaches have been adopted to train and validate Artificial Intelligence techniques in identifying anxiety disorders?

Table 2 presents in detail the application of the PICO model (12) to both research questions, providing a clear description of the key elements for each. This approach not only facilitates the structuring of the literature search and selection process but also ensures that the answers obtained are specific, relevant, and aligned with the objectives set out in the systematic review.

The choice of search terms and date range was based on relevance to the detection of anxiety using AI techniques and the need to include recent research with a high impact on the field of study. The search strategy was developed by Edgar Degante-Aguilar and Giovanni Luna-Chontal. Additionally, articles that do not address anxiety and that focus solely on depression or lack information generated in the experiments were excluded. Any disagreements in the article selection were resolved through discussion and consensus between the two authors. In case of persistent disagreement, a third external arbitrator, an expert in the research area, was designated to make the final decision. Table 3 shows the search strategies in detail, indicating the type of study, database/search engine, and the specification of the terms. Table 3 presents the fundamental search parameters to guarantee that the process is rigorous, reproducible, and exhaustive. They are defined based on the research objective and serve to delimit the scope of the search, identifying the most relevant sources and reducing the risk of bias. Their proper selection and description ensure that the results obtained are representative and relevant to answer the research questions posed.

Table 4 displays the results of an exhaustive search conducted in the PubMed bibliographic database. It focused on identifying the number of scientific articles that explore the intersection between three key areas: artificial intelligence, anxiety disorders, and tasks related to medical information processing. The results are broken down into six sub-equations, each representing a specific combination of search terms using MeSH controlled terminology. The first sub-equation counts the total number of AI-related articles, the second focuses on anxiety disorders, and the third covers a broad set of terms related to tasks such as recognition, detection, diagnosis, classification, prediction, and evaluation. The following 4, 5, and 6 combine the previous terms, with the last two restricting the search to a specific period (2019–2024) and, in the case of Equation 6, filtering the results to include only open access articles. The numbers presented in the results column indicate the number of articles that meet the criteria of each sub-equation, offering a quantitative view of the frequency with which they have been worked on by other researchers.

Likewise, this table presents the results of a search carried out in the ScienceDirect database. This search focused on identifying the number of scientific articles that address the intersection between three main areas: artificial intelligence, anxiety disorders, and tasks related to recognition, detection, classification, or prediction. The results are broken down into five parts, each representing a specific combination of search terms. The first counts the total number of AI-related articles, while the second focuses on anxiety disorders. The third covers a broad set of terms related to data processing tasks. Subequations 4 and 5 combine the previous terms, with the latter restricting the search to the last five years. The numerical results indicate the number of articles found for each subequation, this showing the frequency with which these topics are combined in scientific research.

In the same table presents the results of a search carried out in the SpringerLink database. The results are broken down into six parts, each representing a specific combination of search terms. The first three sub-equations count the total number of articles related to AI, anxiety disorders, and prediction or classification tasks, respectively. The remaining sub-equations function as a filter to specify and narrow down the results. This provides a general idea of the amount of research existing in each of the areas studied.

Additionally, presents the results of a comprehensive search performed in the IEEE Xplore database. The results are broken down into six sub-equations, each representing a specific combination of search terms. The first three sub-equations count the total number of articles related to AI, anxiety disorders, and tasks of recognition, detection, diagnosis, classification, prediction, or evaluation, respectively. The last two sub-equations further refine the search, limiting it to the last five years (2019–2024) and, in the case of sub-equation 6, specifying that the results must correspond to articles published in journals or in early access.

The search terms were carefully established and delimited to be applied in the filters of collected articles. This adjustment allowed for a precise and efficient search of the available literature. As a result, a total of 91 articles were identified in PubMed, 109 from ScienceDirect, 30 from SpringerLink, and 93 from IEEE Explore based on the advanced search equations for each of the databases and search engines of scientific articles. Subsequently, the results were grouped, and duplicate results were eliminated to ensure the relevance of the selected studies. In addition, selection criteria were defined to include academic works consistent with the search terms to facilitate the classification and evaluation of the results, ensuring that only the most relevant and high-quality studies are considered in the systematic review. Articles that do not specifically focus on the identification of anxiety using AI techniques were discarded, as well as those that focused solely on depression without addressing anxiety. Studies that do not apply Machine Learning and Deep Learning algorithms and those that do not provide empirical data or quantitative results were excluded; publications that lack free and/or full access were also excluded. These criteria allowed for a more precise and focused selection of studies relevant to the objectives of the review.

Table 5 presents a set of exclusion criteria for a systematic review of studies related to artificial intelligence applied to anxiety disorders. These criteria, based on the acronym PICO (Patient/Population, Intervention, Comparison, Outcome/Result), serve as guides to select the studies that will be included in the analysis and exclude those that do not meet the established requirements.

For the analysis of the literature, studies that address the identification of anxiety using AI algorithms belonging to Machine Learning and Deep Learning were included. The selected studies provide empirical information and/or quantitative results and are published in peer-reviewed journals in the last five years, from 2019 to 2024, in four selected databases: IEEE Xplora, ScienceDirect, PubMed, and SpringerLink. The studies in English and Spanish encompass a greater diversity of research and full-access publications, allowing for a detailed analysis of their methodologies and findings. The decision to limit the search to articles in English and Spanish is based on the availability of resources and experience of the review team in these languages, recognizing that this may introduce a language bias and exclude relevant studies in other languages. To reduce language bias, we first worked on analyzing the summary/abstract to corroborate whether the information is relevant or not according to the established eligibility criteria in Table 5.

For the data collection process in this systematic review, the PRISMA Statement (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed (13). The data collection from the reports was carried out independently and based on the search terms specified in the methodology, which ensured objectivity and allowed for the identification of discrepancies. Productivity tools such as reference management software (14) and scientific article databases were used to identify and extract relevant information, facilitating the organization and analysis of large volumes of data. The collected data were recorded in an integrating table of references, and relevant information was extracted from each of the studies. To ensure uniformity in the collection of information, standardized templates and guides were developed that all reviewers followed rigorously. This structured and systematic approach allowed for a comprehensive and accurate data collection, in line with the best practices recommended by the PRISMA Statement.

Specific data related to the identification of anxiety disorders using Artificial Intelligence techniques were sought, focusing on key aspects such as AI algorithms, CNN, and other specific algorithms most frequently used in the selected studies. Validation data for the proposed methods were collected, including cross-validation, independent datasets, and training-test division, which allowed for the evaluation of the dimension and effectiveness of the implemented algorithms. The time points at which the results were measured were also recorded, such as the initial moment, during follow-up, and at the end of the study. This information allowed for the evaluation of the consistency and stability of the models over time. Data collection included a description of the methods used, specifying the sample size, the origin of the data (e.g., clinical data, self-reports, sensor data), and the type of data (structured or unstructured). This characterization of the data is crucial to understanding the context and applicability of the results. The inclusion criteria for the studies were based on the specificity of the approach to anxiety identification, the application of AI techniques, and the provision of quantitative results.

Likewise, data on any secondary analyzes performed in the studies, such as subgroup analyzes, adjustments for confounding variables, and sensitivity analyzes, were collected. This additional information allowed the analysis of factors that may influence the results. Therefore, studies with detailed descriptions and well-documented methodologies were prioritized, ensuring the reliability and relevance of the information gathered in this systematic review.

The reviewers independently and blindly evaluated the selected publications, ensuring that the work carried out was reliable. The Zotero reference management system (14) was used to organize the studies and facilitate the analysis of the sections of each selected study.

The process of this systematic review required a limited and defined method according to the eligibility criteria. The first step consisted of an exhaustive search in the IEEE, Elsevier, Scielo, and PubMed databases using query formulas with logical operators to guarantee a relevant range. Once all the studies were collected, duplicates were removed to ensure the integrity of the information gathered. The next step consisted of filtering the titles and content of the abstracts of all the selected studies, and those that met criteria CI1 (the publication directly addresses anxiety) and CI2 (the objectives related to the diagnosis and/or treatment of anxiety are evident in the abstract) were considered for the next stage.

The studies that passed the initial filter were read in their entirety. At this stage, it was necessary to apply CI3 (the authors’ contribution is significant to complement the systematic analysis) and CI4 (the use of Artificial Intelligence techniques for the diagnosis and/or treatment of anxiety is mentioned). To facilitate systematic comparison, detailed tables were created that included the key characteristics of each study, such as information on AI techniques, samples, results, study design, and validation methods, among others.

Figure 1 presents the PRISMA flow diagram (13) to illustrate the article selection process in a systematic review. It begins with the identification of studies through databases and records, which are subjected to an initial screening process to remove duplicates or those clearly not relevant. Next, the full text of the selected articles is obtained for a more detailed assessment of their eligibility, considering predefined criteria such as the content of the article or the type of publication. Finally, 119 studies included in the review are presented, thus demonstrating the transparency and rigor of the selection process carried out based on following inclusion criteria: studies include information specify of anxiety disorders (CI1), publications that demonstrate the application AI for the diagnosis anxiety disorders (CI2), information obtained of RQ1 and RQ2 contrast (CI3) and CI4–CI7 that contains common filters. The exclusion criteria has been applied in this Systematic Review after screening process. The following exclusion criteria where applied: studies with populations that are not diagnosed with anxiety disorders corresponding to CE1, publications does not make contributions in Artificial Intelligence (CE2), studies that do not include relevant information (CE3), studies that provide qualitative results without metrics (CE4) and CE5–CE8 that contains basic filters as last five years.

Figure 2 reflects the number of studies, related to the search terms, published by country from January 2019 to September 2024. The concentration of studies on the application of artificial intelligence in mental health research is significant, particularly in China. This country, along with India and the United States, leads the vanguard in the development of innovative technological solutions for the diagnosis, treatment, and monitoring of mental disorders. The more intense coloration in these regions indicates a substantial investment in research and development in this emerging field.

The countries marked with a darker color indicate a greater number of published articles, among which China stands out with 35 studies, the United States with 22, and other countries with similar numbers. The trend of published studies indicates that in the northern hemisphere there is a constant research task within the study area and corresponds to the large number of technological news announced in various media. The following Artificial Intelligence techniques have been used in China: Natural Language Processing based on Machine Learning, DNN to identify complex characteristics in images and physiological signals within the diagnosis of anxiety. Similarly, CNN was used with the variant of identifying emotions in audios, others used predictive models of behavior in social networks. Very similar techniques were used in the United States, but with a focus on the analysis of emotions and anxiety disorders in social networks and clinical data in order to guarantee the reliability of the published results. In the rest of the countries, physiological signals, behavioral signals, and the fusion of characteristics applied to multimodal data were included.

Table 6 reflects how the predominant countries in the selected research area have contributed to improving the diagnosis of anxiety disorders. China and the United States stand out for using Machine Learning and Deep Learning in diverse populations made up of social media users (15–18) and patients with previous diagnoses of anxiety. Slovakia uses Deep Learning for the classification of emotions (arousal and valence) (19) with physiological signals, obtaining high accuracy results in the prediction of emotional states (12). Taiwan and Spain apply Deep Learning and Machine Learning techniques to clinical data (20), with the aim of improving the diagnostic accuracy of anxiety disorders by analyzing environmental factors and personal data.

In the results of the application of PRISMA Statement (13), a total of 293 studies were identified from a database and three scientific article search engines. In the filtering process, 293 studies were selected to be reviewed more comprehensively, with a result of 37 articles excluded for not meeting the eligibility criteria defined in Table 9. Of the 256 studies that passed the selection phase, 119 studies were selected to be evaluated in depth to determine their eligibility. In this process, 94 studies were excluded due to various reasons: 70 for not providing relevant information, 18 for being an inappropriate type of publication, and 6 for being irrelevant. After the eligibility evaluation process, 119 studies were selected and included in this systematic review for analysis.

Of the 119 selected studies, it was identified that the majority use Artificial Intelligence techniques, including machine learning and deep learning, to identify anxiety disorders (21). These studies were based on self-reports (22), physiological data, and social media posts (70). Deep learning was used more in the studies examined due to the wide range of applications and the accuracy of the algorithms. As a result, it was found that these models were effective in detecting anxiety with great precision in contrast to traditional diagnostic tools that presented confidence biases in some cases. With the Systematic Review, some limitations were found in current research, including variability in the methods and data used, and possible bias in the training data.

Table 7 provides details of various studies that apply both Machine Learning and Deep Learning techniques in diverse populations for the identification of anxiety disorders, aligning with the research questions defined above. The filtered studies include from social media users (9, 23), to participants with anxiety disorders identified through neuroimaging (11) and patients with clinical data (24). This allows us to evaluate how different algorithms respond to heterogeneous populations, thus addressing research question RQ1. Interventions vary in the application of different Machine Learning and Deep Learning algorithms, focusing on the classification of anxiety disorders using social media (23, 25), while others apply methods such as the construction of emotional lexicons from large amounts of textual data (18), or the use of facial images and audio to assess mood states (26).

Several studies in Table 7 include comparisons with traditional diagnostic methods or without a direct comparator, but also between different AI approaches. For example, in China one study identified an anxiety identification method based on deep features that demonstrated superiority over traditional methods. Similarly, in Taiwan, a study evaluated the diagnostic accuracy of an AI system using clinical data, obtaining a 72.4% accuracy and comparing it with standard clinical metrics. Chiu et al. (27), which clearly answers RQ2 by identifying the techniques that provide the greatest benefits in terms of diagnostic accuracy compared to traditional methods. Likewise, the results indicate significant improvements in the accuracy, sensitivity, and specificity of the proposed models, as observed in studies using Deep Learning (28, 29) and CNN (26). Another example is the classification of emotions using Deep Learning in Slovakia (2022), where high-precision results were obtained in predicting emotional states from physiological signals. These findings not only align with RQ2 by demonstrating which techniques optimize diagnostic tools, but also contribute to answering RQ1 by showing the high performance of these techniques in diverse populations and with varied data types.

Table 8 contains a summary of studies on Artificial Intelligence techniques used for the diagnosis of anxiety disorders, highlighting key metrics such as accuracy, sensitivity, and specificity. The use of Deep Learning demonstrates a more efficient performance in contrast to the handling of unstructured and multimodal data, as shown by studies conducted in China (2020) and Slovakia (2023). On the other hand, Machine Learning algorithms have also proven to be effective, especially in the analysis of brain images and texts, obtaining an accuracy of 90% in the detection of neurocognitive abnormalities in patients with anxiety and depression disorders. The use of Deep Learning for the classification of emotions in Slovakia (2023) was particularly effective, reaching accuracies of 87.88% in the classification of arousal and 85.61% in valence. This highlights how deep neural networks can process physiological signals to detect emotional states, which can be useful in the early identification of anxiety.

Table 9 offers a global view of the various Artificial Intelligence (AI) techniques and algorithms used in research worldwide registered in the databases cited in this article between 2019 and 2024. Ranging from deep neural networks such as CNN and LSTM to machine learning algorithms such as Random Forest and SVM, the table illustrates the diversity of approaches used in AI. The combined use of CNN-LSTM-CNN in the United Kingdom, Deep Belief Networks (DBN) together with Soft-max Regression and the Limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm in Pakistan, and the implementation of the MACBETH method in Brazil (2019) are highlighted.

Table 10 describes a selection of Deep Learning algorithms, a subcategory of machine learning that focuses on the use of artificial neural networks with multiple layers to analyze data and extract complex patterns. The table presents algorithms such as LSTM, used for sequence analysis; Q-Learning, a reinforcement learning algorithm; and CNN-LSTM-CNN, an architecture that combines convolutional and recurrent neural networks. It also includes machine learning algorithms such as SVM, Naive Bayes, and Decision Tree, which, although not exclusive to Deep Learning, are often used in conjunction with deep neural networks to improve their performance.