Enhancing mental health with Artificial Intelligence: Current trends and future prospects

Artificial Intelligence (AI) has emerged as a transformative force in various fields


Introduction
The convergence of Artificial Intelligence (AI) and mental healthcare fields marks a significant transformation in healthcare [1,2].The backdrop against this transformation is the evolving paradigm of mental health.What was once stigmatized and often overlooked is now recognized as a crucial dimension of overall well-being.However, this emerging awareness has also unveiled the scale of the mental health crisis that plagues societies worldwide.According to the World Health Organization (WHO), mental health disorders are now a substantial contributor to the global disease burden, with depression alone representing the leading cause of disability globally [3].The surge in the prevalence of mental health disorders has placed an unprecedented demand on healthcare systems, revealing the inadequacies of traditional models of mental health care [4,5].The conventional approach, which heavily relies on in-person consultations and therapies, falls short of addressing the increasing demand for accessible, affordable, and easily expandable mental health services [4].This disparity between the demand for and supply of mental healthcare highlights the pressing need for innovative solutions.
AI possesses remarkable capabilities, such as efficiently handling extensive datasets and facilitating the examination of complex patterns and relationships [6].In the context of mental healthcare, where understanding complex human behaviors and emotions is paramount, AI offers the potential to revolutionize mental healthcare by providing insights and solutions that were previously beyond the reach of conventional methods [7,8].It is a transformative tool that offers advanced detection approaches, tailored therapies, and virtual therapeutic platforms.It would potentially broaden the availability of healthcare, reduce stigma, and improve treatment outcomes [9,10].
Integrating AI into mental healthcare is reshaping the landscape of mental healthcare; it represents an evolution and a revolution in mental well-being [11].However, while this transformation presents the potential for widespread access, facilitating early intervention, and personalizing treatments, it also gives rise to ethical considerations, regulatory challenges, and the need for ongoing research and development [12][13][14].As the diverse range of applications and implications of AI in this field are explored further, the synergy between human expertise and AI capabilities holds the potential to usher in a new era of mental healthcare.
The escalating burden of mental health issues globally is nothing short of a pandemic, contributing to approximately 16 % of the global disease burden [15].With prominent mental ailments such as depression and anxiety costing the global economy about 1 trillion USD annually in lost productivity, the urgency for effective solutions cannot be overstated [16].The pervasive stigma surrounding mental health exacerbates the crisis, leaving countless individuals without the necessary care and contributing further to a cycle of neglect and suffering.However, the advent of Artificial Intelligence (AI) in healthcare presents a beacon of hope.By integrating AI into mental health services, there is a tangible opportunity to not only mitigate the effects of this global pandemic but to transform the landscape of mental health care.AI's potential to enhance early detection, provide personalized treatment options, and offer support through innovative platforms could revolutionize how we approach mental wellness, making care more accessible and less stigmatized.This narrative review arrives at a critical juncture.As the AI boom unfolds globally, it is imperative to assess both the strides made in the AI and mental health field and to anticipate the challenges and opportunities that lie ahead.By exploring the progress, prospects, and potential pitfalls of integrating AI into mental health care, this review aims to underscore the significance of this fusion in addressing one of the most pressing health crises of our time.

Methods
This paper adopts a narrative review approach to comprehensively investigate the utilization of Artificial Intelligence (AI) in mental healthcare.The screening and eligibility criteria for paper selection involved inclusion of papers published in peer-reviewed journals, conference proceedings, or reputable online databases, focusing on the application of AI in mental healthcare, including review papers providing an overview, analysis, or synthesis of existing literature.Exclusion criteria encompassed papers failing to meet the inclusion criteria, duplicates, non-English publications, or those unrelated to the review topic.The screening process consisted of three stages: title screening, abstract screening, and full-text eligibility assessment, with papers not meeting inclusion criteria being excluded at each stage.The search strategy aimed to identify relevant papers published on "Artificial Intelligence in Mental Healthcare" between January 2019 and December 2023, encompassing academic journals, conference proceedings, and reputable online databases.Following selection, review papers underwent further analysis for pertinent information, trends, examples, and ethical considerations concerning AI in mental healthcare.

Result
A total of 211 papers were found in four database searches, out of which 87 publications were excluded due to non-English language and duplicates.Following the abstract and title screening, 32 articles were eliminated as they did not fulfill the eligibility criteria.Therefore, this review included a total of 92 eligible studies.(See Table 1).

History of AI in mental healthcare
The journey of AI's integration into mental healthcare can be traced back to the mid-20th century, a period marked by the emergence of the computing era, when scientists began to envision the possibility of robots imitating cognitive processes, thereby setting the stage for further advancement in this field [17][18][19].In the 1950s and 1960s, AI pioneers Allen Newell and Herbert A. Simon embarked on groundbreaking research, aiming to develop AI models of human problem-solving [17,18,20,21].Their work laid the foundational concepts of symbolic AI, later proving instrumental in simulating cognitive processes in mental health contexts [21].Though rudimentary by today's standards, this early AI research laid the foundation for a significant convergence of AI and psychology.
By the late 1960s and early 1970s, Joseph Weizenbaum created one of the earliest AI applications in psychology [18].His program, ELIZA, was a chatbot that simulated a Rogerian psychotherapist [22].While ELIZA's responses were relatively simplistic, they could engage users in text-based conversations, providing a glimpse into the potential for technology to support mental health interactions [22,23].The use of AI in mental healthcare over the decades has experienced a progressive expansion.The development of expert systems, rule-based AI systems designed to emulate human expertise, commenced in the 1980s [11,24].These systems aimed to provide diagnostic and treatment recommendations across several psychological domains [1,25].Although the skills of the early AI systems were somewhat limited compared to modern AI, they represented a notable advancement in integrating technology and mental health [26,27].
The late 20th century witnessed the emergence of computerized cognitive-behavioral therapy (CBT) programs [28,29].These interactive software applications aim to provide evidence-based therapy interventions for prevalent mental health conditions.Although the initial efforts were somewhat rudimentary compared to today's AI-powered interventions, they signified a transition into utilizing technology to enhance the accessibility of mental healthcare [28].As computing power advanced, AI's role in mental healthcare evolved exponentially.The advancement of AI in the 21st century has encompassed various aspects of mental healthcare, including early identification of mental health problems, individualized treatment plans, virtual therapists, advances in teletherapy, and continuous monitoring [9,10].These contemporary applications of AI have the potential to revolutionize the field by making mental health care more accessible, effective, and data-driven.Overall, the history of AI in mental healthcare is marked by a series of evolutionary milestones, from early cognitive modeling to today's advanced AI-driven interventions [30].This journey reflects the ever-growing recognition of technology's potential to support and enhance mental well-being, suggesting a future where AI plays an integral role in addressing the global mental health crisis.Table 2 below highlights specific AI tools used in current mental healthcare.

The role of AI in diagnosis
AI has emerged as a valuable tool in the early detection and prediction of mental health disorders [30].These technologies, whether analyzing speech, text, facial expressions, or electronic health records, are transforming how mental health is diagnosed and managed [1,51].With the aid of predictive models, AI enhances early intervention, personalizes treatment plans, and improves overall mental well-being [10,52].Fig. 1 highlights the application of AI in mental health diagnosis.

Early detection of mental health disorders
AI-driven tools have made significant strides in analyzing speech, text, and facial expressions to identify early signs of mental health disorders.Natural Language Processing (NLP) techniques enable extracting valuable insights from written or spoken words [53,54].For example, sentiment analysis can identify nuanced changes in an individual's emotional condition by examining social media posts, chat logs, or written diaries [55].
Moreover, voice analysis can detect alterations in speech patterns, encompassing variations in pitch, tone, and rhythm, which might potentially serve as indicators of anxiety, depression, or other mental health conditions [56,57].Facial expression analysis, often coupled with computer vision, can provide insights into an individual's emotional Wysa is a chatbot that provides therapy support for a variety of mental health conditions, including depression, anxiety, stress, and loneliness.It uses a combination of CBT, mindfulness, and positive psychology to help users improve their mental health.[32] 3. Talkspace Talkspace is an online therapy platform connecting patients with licensed therapists through video, text, and audio messaging.It uses AI to match patients with therapists best suited to their needs.[34]

BetterHelp
BetterHelp is an online therapy platform that connects patients with licensed therapists.It uses AI to match patients with therapists but offers a broader range of therapeutic approaches, including cognitive-behavioral therapy (CBT) and psychodynamic therapy.[33] AI tools Emotional health apps

Moodfit
Moodfit is an app that uses AI to track and analyze users' moods and emotions.It can help users to identify patterns in their moods and to develop strategies for managing their emotions.[35]

Happify
Happify is an app that uses AI to help users build resilience and happiness.It offers a variety of games, activities, and exercises designed to improve users' mood, well-being, and resilience.[36]

Headspace
Headspace is an app that offers guided meditation and mindfulness exercises.It uses AI to personalize the meditation experience for each user.[  state [58].AI systems can detect micro-expressions and subtle changes in facial features that may indicate underlying psychological conditions [59].This technology benefits remote mental health monitoring through video consultations or mobile apps.The current trend in this domain involves advancing AI algorithms capable of discerning and comprehending these subtle indicators with notable precision.As AI systems continue to improve, they hold the potential to provide early warning signs of mental health disorders, allowing for timely intervention and support.The AI-driven mental health app "Woebot" employs sentiment analysis to analyze user text input [31].If a user consistently expresses sadness, hopelessness, or despair in their chat interactions, Woebot recognizes these patterns and offers guidance or recommends professional help.In voice analysis, "Cogito," an AI platform used in telehealth services, monitors patient speech changes during therapy sessions [60,61].If there are shifts in pitch, tone, or rhythm that may indicate anxiety or depression, Cogito alerts the therapist to address these emotional cues during the session.Facial expression analysis is another valuable tool in early detection."Affectiva," a pioneering company in emotion AI, has developed facial expression analysis tools [62].These tools have been employed in various mental health studies.For instance, researchers used Affectiva's technology to examine the facial expressions of individuals with depression, shedding light on potential early diagnostic markers [63].AI has also shown promise in analyzing electronic health records (EHRs) to aid in the early diagnosis of mental health disorders [52,64].Machine learning algorithms can sift through vast patient data, including medical histories, diagnostic tests, and clinical notes, to identify patterns suggesting a mental health condition [65,66].These algorithms can flag patients at risk, ensuring that healthcare providers pay closer attention to their mental well-being during routine care.Additionally, AI can facilitate the integration of mental health data into a patient's overall health profile, enabling a more holistic approach to healthcare [67,68].This trend toward personalized medicine aids early detection as it considers an individual's unique medical history, genetics, and lifestyle factors.
The "Google Depression Screening Tool" is a prime example [69].When users search for depression-related terms on Google, the tool prompts users to complete the Patient Health Questionnaire-9 (PHQ-9), a clinically validated questionnaire [69][70][71].Based on the user's responses, it provides guidance on seeking professional help if necessary.This integration into one of the world's most widely used search engines significantly increases the potential reach of mental health assessments.

Predictive modeling
AI is increasingly used to develop multifactorial predictive models for mental health [1,51,72].These models consider a wide range of factors, including genetics, environmental factors, lifestyle choices, and social determinants of health.By combining these variables, AI can predict an individual's risk of developing a mental health condition.For example, genetic information can provide insights into predispositions, while environmental factors such as traumatic events or social isolation can be integrated to enhance accuracy further.The mental health platform "Ginger" utilizes predictive analytics to identify individuals at risk of developing mental health conditions [73].By analyzing usage patterns and assessment responses, Ginger proactively reaches out to users who may require additional support.For example, if a user displays a combination of behaviors such as increased stress levels, disrupted sleep patterns, and social withdrawal, Ginger's predictive model may prompt a mental health coach to offer assistance [74].
One of the notable trends in predictive modeling is the integration of wearable technology and mobile health apps [28,75].These tools collect real-time data on individuals' behaviors, such as sleep patterns, physical activity, and social interactions, which can be invaluable for creating accurate predictive models [72].
Predictive modeling helps identify individuals at risk and improves treatment outcomes.AI-driven models can predict how a patient may respond to different treatment approaches, whether psychotherapy, medication, or lifestyle changes [10,76].This personalized approach ensures that individuals receive tailored interventions, optimizing the chances of recovery and minimizing the risk of adverse effects [77].Furthermore, predictive models have the capability to forecast disease progression, thereby assisting healthcare providers in making well-informed decisions about treatment plans and resource allocation [78].By predicting the probable course of mental health disorders, healthcare systems can enhance their readiness to address the requirements of mental health services and effectively allocate resources accordingly.For example, IBM's "Watson for Drug Discovery" uses AI to analyze vast genetic and chemical information datasets to identify potential drug candidates for mental health conditions such as schizophrenia and bipolar disorder [79].This accelerates drug development, potentially offering more effective treatments for these conditions.

AI in treatment
AI is reshaping the landscape of mental health treatment through personalized interventions and the rise of virtual therapists and chatbots [80][81][82].These trends signify a pivotal shift towards potentially more effective, accessible, and scalable mental healthcare.Fig. 2 below highlights the application of AI in mental health treatment.

Personalized treatment plans
Integrating AI into mental health treatment has brought a profound shift toward personalized interventions.Fueled by vast datasets and machine learning capabilities, AI algorithms can analyze an individual's unique characteristics and needs [83,84].This includes genetic predispositions, past treatment responses, behavioral patterns, and real-time physiological data.This comprehensive analysis allows AI to customize treatment plans unprecedentedly, ensuring appropriate interventions are matched to individual patients.AI can analyze a patient's genetic makeup to predict their response to various antidepressant medications [85,86].For example, a patient with a specific genetic profile may be more likely to respond positively to a particular class of antidepressants.Personalized treatment plans can significantly enhance the chances of recovery and minimize the side effects of ineffective medications.
The impact of personalized treatment plans on therapy efficacy could be significant.Traditionally, mental health treatments have followed a one-size-fits-all approach, sometimes leading to suboptimal outcomes.AI-driven personalization enables therapists to design interventions that align precisely with an individual's specific challenges and strengths [87,88].This leads to more effective treatments, shorter recovery times, and improved patient satisfaction.In the context of addiction treatment, AI can continuously observe and analyze a patient's behavioral patterns, such as triggers, stressors, and substance use [89,90].AI can alert therapists and patients to potential relapse risks in real-time by identifying high-risk situations.This proactive approach allows for timely interventions and the modification of treatment strategies.
AI is also pivotal in guiding treatment decisions throughout the therapeutic journey.Algorithms continuously analyze patient progress, adjusting treatment plans in real-time based on evolving needs and responses [91][92][93].This dynamic approach minimizes the trial-and-error often associated with mental health treatments, optimizing the therapeutic process and maximizing the chances of success.AI-driven algorithms can adapt CBT interventions based on the patient's progress and unique cognitive patterns [94,95].For instance, if a patient exhibits perfectionist tendencies, the AI may effectively adapt the therapeutic approach to address these traits.This tailored approach enhances the effectiveness of CBT for different individuals.

Virtual therapists and chatbots
Virtual therapists and AI-powered chatbots represent a significant trend in enhancing the accessibility of mental health resources [96,97].These digital entities provide around-the-clock support to individuals with mental health concerns, irrespective of geographical or time constraints.This accessibility addresses a critical gap in mental healthcare, ensuring individuals can seek help whenever needed.Crisis hotlines are utilizing AI-powered chatbots to provide immediate support to distressed individuals.These chatbots can engage in empathetic conversations, offer coping strategies, and connect users with human therapists or crisis helplines when necessary.For instance, Crisis Text Line employs a chatbot that has handled millions of conversations and provided support during moments of crisis [98].
Virtual therapists and chatbots provide a discreet and stigma-free platform for individuals to engage with mental health support [96].Many people hesitate to seek in-person therapy due to the perceived stigma associated with mental health.AI-driven solutions address this concern, allowing users to receive support in the privacy of their own space, thereby reducing barriers to care.These virtual therapists engage users in text-based conversations, providing evidence-based interventions such as cognitive-behavioral techniques.Users can access these resources discreetly and conveniently, promoting regular self-care.
AI-driven virtual therapists and chatbots offer scalable solutions to the growing demand for mental health support.With the ability to interact with multiple users simultaneously, these digital entities provide cost-effective alternatives to traditional therapy.This scalability is particularly crucial in addressing the shortage of mental health professionals.For instance, AI-driven virtual therapists have been developed to provide therapy for children with autism spectrum disorder [99][100][101].These virtual therapists use facial recognition technology to analyze a child's facial expressions and adjust their interactions accordingly.They can teach emotional recognition and social skills in a controlled and supportive environment.The sophistication of conversational AI models enables these to engage in empathetic and therapeutic dialogues.These chatbots can actively listen, provide emotional support, and even deliver cognitive-behavioral interventions.Individuals gain access to a broader spectrum of mental health resources by leveraging AI for emotional support.

AI in therapy delivery
The integration of AI into therapy delivery is reshaping the mental healthcare field, presenting novel approaches that improve the effectiveness of therapy sessions and amplify the ability of therapists [6,102].These trends make mental healthcare more accessible, effective, and data-driven.The collaboration between AI and therapists may improve treatment outcomes and enhance the overall quality of mental health care.

Teletherapy enhancement
AI potentially enhances the quality of teletherapy sessions by analyzing patient emotions in real-time.For example, AI algorithms can analyze facial expressions, voice tone, and speech patterns to gauge a patient's emotional state during a video therapy session [103,104].This analysis provides therapists valuable insights, allowing them to adjust their approach and interventions based on the patient's emotional cues.For instance, Kintsugi is an AI-driven teletherapy platform that utilizes facial and voice analysis to provide real-time emotional feedback to therapists [45].By analyzing the patient's facial expressions and voice tone, Kintsugi helps therapists understand the patient's emotional state, improving the therapeutic process.
AI-powered teletherapy transcends geographical barriers, making mental healthcare more accessible to individuals in remote or underserved areas [52,105].Through secure video conferencing platforms augmented with AI, patients can connect with qualified therapists, ensuring they receive the support they need, regardless of location [106].For instance, BetterHelp is an online counseling platform that utilizes AI to match patients with licensed therapists [34].It offers teletherapy sessions, enabling individuals to access mental health support from the comfort of their homes, irrespective of their geographical location.

Therapist assistance
AI assists therapists by analyzing vast datasets, including patient histories, treatment responses, and progress reports [92,107].AI can provide therapists with data-driven insights that inform treatment decisions and interventions by identifying patterns and trends in this data.Cerebral is an online mental health platform that employs AI to support therapists in their practice [47].It analyzes patient data and provides therapists with insights into treatment progress and potential areas for adjustment.This collaborative approach enhances the therapist's ability to make informed decisions and tailor treatments effectively.
AI can suggest interventions and treatment strategies based on the patient's profile and progress [92].For instance, if a patient has not responded well to a particular therapy approach, AI can recommend alternative strategies that may be more effective.Wysa is an AI-powered mental health chatbot that offers therapeutic interventions based on cognitive-behavioral principles [32].It suggests coping strategies and exercises to users based on their interactions, providing immediate support and actionable recommendations.
AI collaborates with therapists to extend their capabilities.By automating certain administrative tasks and offering real-time data analysis, AI allows therapists to focus more on the therapeutic aspects of their work, ultimately improving treatment outcomes [67].Talkspace is an online therapy platform that incorporates AI to assist therapists in managing their caseloads efficiently [33].It handles administrative tasks such as scheduling and billing, allowing therapists to dedicate more time to their patients.AI also supports therapists in providing evidence-based interventions and tracking progress.

AI in monitoring and follow-up
AI's role in monitoring and outcome assessment in mental healthcare is a game-changer.Continuous monitoring powered by AI-enabled devices offers early detection of relapses and a deeper understanding of patients' mental health patterns [108].AI-driven outcome assessments provide objective measurements that guide data-driven decisions for treatment plans, ultimately improving treatment efficiency and patient outcomes [77].

Continuous monitoring
Wearable devices equipped with AI technology are emerging as powerful tools for continuously monitoring physiological and behavioral markers indicative of mental health status [109,110].These devices, such as smartwatches and fitness trackers, collect data on heart rate variability, sleep patterns, physical activity, and speech patterns.AI algorithms analyze this data to identify deviations from baseline, offering early insights into changes in mental health.For example, The Oura Ring is a wearable device that employs AI to monitor sleep patterns, activity levels, and physiological metrics like heart rate variability [111].It can detect changes in a user's sleep quality and patterns, which may indicate stress or mood disturbances.Also, Mindstrong Health has developed an app that uses AI to analyze smartphone keyboard interactions, such as typing speed and errors [48].These patterns can indicate changes in cognitive function and mental health.The app offers insights into the user's mental well-being by continuously monitoring these interactions.
Continuous monitoring powered by AI enables the early detection of relapses or deteriorations in mental health [77,112].For individuals with conditions like depression or bipolar disorder, changes in sleep patterns, physical activity, or speech can serve as early warning signs.AI algorithms can flag these changes, alerting patients and healthcare providers for timely intervention.Continuous monitoring facilitates early detection and supports long-term mental health care.By collecting a wealth of data over time, AI helps therapists and patients better understand the patient's mental health patterns [51,112,113].This information provides crucial insights for making treatment decisions and facilitates the development of more effective, personalized interventions.

Outcome assessment
AI-driven assessments provide objective measurements of treatment progress and effectiveness.These assessments extend beyond traditional self-report questionnaires and incorporate data from various sources, such as patient surveys, physiological data, and behavioral observations [93,112].AI analyzes this data to gauge treatment outcomes.For example, reSET is a US Food and Drug Administration-approved prescription digital therapeutic for substance use disorder [114].It uses AI to track patient engagement and completion of therapeutic modules.The data generated by reSET enables therapists and patients to assess treatment progress objectively and adjust interventions as needed.
AI-generated outcome assessments empower therapists to make data-driven decisions regarding treatment plans [10,92].Therapists can track the effectiveness of interventions in real-time and adjust treatment strategies based on the patient's progress.This approach minimizes the reliance on subjective evaluations and has the potential to enhance the precision of therapeutic decisions.This may accelerate the path to recovery and reduce the potential for patients to undergo prolonged, ineffective treatments.

Ethical considerations in AI for mental healthcare
It is crucial to balance the advantages of AI with ethical considerations, ensuring that these technological advancements are harnessed responsibly and in the best interest of patients [31].Addressing ethical considerations in AI for mental healthcare is crucial to ensure responsible and effective use of these technologies.Privacy and data security measures, bias mitigation, and preserving the human element in therapy are pivotal in building trust, reducing disparities, and providing ethical and high-quality mental health care [16,115].As AI technologies advance, ongoing vigilance and adherence to ethical principles will remain central to their integration into mental healthcare practices.
One of the primary challenges in the field is the absence of clear and comprehensive regulatory frameworks governing AI's use in mental health [116,117].The regulatory landscape is evolving to address ethical, privacy, and safety concerns and ensure that AI applications meet rigorous standards.For example, the FDA has begun to regulate certain AI-based medical devices, including those used in mental health [118].Clear guidelines and requirements for safety and effectiveness help ensure that AI-driven tools meet appropriate standards.International efforts are underway to harmonize regulatory approaches for AI in healthcare.Organizations and governments are collaborating to establish common principles and guidelines for responsible AI use in mental health therapy.

Human-AI interaction
Maintaining the human element in therapy while leveraging AI as a tool is a critical ethical consideration.AI should enhance, not replace, the therapeutic relationship between patients and therapists [88].Striking the right balance between AI-driven interventions and human care is essential.For example, Woebot is designed to serve as a supplementary tool to therapy rather than substitute it.This offers support and help to patients, bridging the gap between therapy sessions while maintaining the human-therapeutic relationship [31].
Patients should be informed when AI tools are part of their therapy.Transparency about the role of AI in their treatment allows patients to make informed decisions about their care and understand the extent of AI's involvement [119].AI-driven continuous monitoring should include human oversight.While AI can detect changes in patient behavior, therapists should interpret and act on these insights, ensuring that the human touch remains central to care.

Privacy and data security
Using AI in mental health interventions necessitates stringent measures for protecting patient data and ensuring confidentiality [120].It is imperative to safeguard sensitive information, such as medical histories, therapy session records, and behavioral data, from unauthorized access or breaches.For example, many AI-driven mental health platforms, like Talkspace, adhere to the Health Insurance Portability and Accountability Act (HIPAA) regulations of 1996 [33,121].This ensures that patient data is securely stored and transmitted, maintaining the privacy and confidentiality of therapy sessions.Ethical AI in mental healthcare also involves addressing questions of data ownership and obtaining informed consent from patients [16].Individuals should have precise control over their data and understand how it will be used in AI-driven interventions.

Bias and fairness
Bias and fairness are fundamental factors in improving mental health with AI.Biases in training data or algorithms can result in unfair treatment, prolonging disparities in mental health diagnosis, healthcare access, and treatment outcomes [115,122].For example, if AI models are primarily trained on data from specific demographic groups, they may not adequately represent the diversity of mental health experiences across different populations.Consequently, underrepresented communities may experience misdiagnosis, insufficient treatment recommendations, or even worsening of their mental health conditions [122,123].
It is crucial to diversify training data, consistently access AI systems for discriminatory results, and incorporate transparency and accountability measures into algorithmic development to mitigate bias and promote fairness [123][124][125].Additionally, incorporating a wide range of participants, such as mental health professionals and marginalized communities, in the development and assessment of AI tools can help to reduce bias and ensure AI-based driven interventions are ethical, efficient, and fair for all individuals seeking mental health assistance [126].

Implications of research
The utilization of AI in improving mental health has wide-ranging implications across practice, research, prevention, and policy.In practice, AI-powered tools provide scalable and personalized interventions, effectively expanding mental health care to underserved populations.Nevertheless, practitioners must receive training to employ these technologies and uphold ethical principles proficiently.AI enables the analysis of extensive datasets in research, allowing for identifying patterns, treatment improvement, and creating prediction models to enable early intervention.However, it is crucial to prioritize data privacy and address algorithm biases.
For prevention, AI can identify risk factors and provide timely interventions, potentially reducing the strain on healthcare systems.However, it is essential for regulations to establish measures that prevent the improper use of data and guarantee fair and equal availability of AI-powered mental health services.Policy necessitates developing regulations that govern AI's ethical utilization in mental health, encompassing data protection, transparency, and accountability measures.Effective collaboration among stakeholders is crucial to leverage the potential of AI while minimizing its hazards entirely.

Strengths and limitations
AI presents potential solutions for improving mental health through personalized interventions, early detection of symptoms, and virtual therapy platforms.Its strength lies in its capacity to rapidly analyze extensive data, providing valuable insights and forecasting potential mental health concerns.AI-powered chatbots and virtual therapists can offer continuous support, reaching a wider demographic at reduced costs, diminishing social stigma, and enhancing availability.
Nevertheless, AI in the field of mental health has certain limitations.Firstly, the importance of privacy considerations related to the sensitive nature of mental health data cannot be overstated.Algorithm bias poses a potential risk since it may result in insufficient or unsuitable assistance for specific populations.Moreover, AI is devoid of human empathy and comprehension, which are vital in therapeutic interactions.Further limitations arise from the need to integrate with preexisting healthcare systems and navigate regulatory difficulties.Hence, although AI possesses considerable potential in mental health care, it is crucial to deliberate its limitations to ensure responsible and effective implementation.

Conclusion
The challenges and future directions in AI for mental healthcare are dynamic and multifaceted.Establishing robust regulatory frameworks, ensuring model validation and transparency, and investing in continuous research and development are crucial steps toward harnessing the full potential of AI in improving mental healthcare.As AI technologies continue to evolve, these efforts will play a pivotal role in shaping the future of mental health therapy, making it more accessible, practical, and ethical for individuals.
The validation and transparency of AI models used in clinical settings are crucial.Rigorous testing and validation processes are necessary to ensure that AI-driven interventions are accurate, reliable, and safe for patients.AI models that have undergone extensive clinical trials and validation are gaining prominence.These models are based on evidencebased practices and have demonstrated their efficacy in improving mental health outcomes.Developing AI models that are interpretable and can provide explanations for their recommendations is a growing trend.This promotes transparency and allows clinicians and patients better to understand the reasoning behind AI-generated insights and decisions.

Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Fig. 1 .
Fig. 1.Application of AI in mental health diagnosis.

Fig. 2 .
Fig. 2. Application of AI in mental health treatment.

Table 1
Keyword search and paper selection process.

Table 2
AI tools used in current mental healthcare.
AI toolsChatbot-based therapy 1. Woebot Woebot is a chatbot that provides CBT-based therapy for depression and anxiety.It has been shown to be effective in reducing symptoms of depression and anxiety in clinical trials.[31] 2. Wysa [44]ffers a variety of interactive exercises and tools to help users manage their symptoms and improve their mood.[42]9.PTSD Coach PTSD Coach is an app that provides users with tools and resources to help them manage post-traumatic stress disorder (PTSD), a mental health condition that can develop in people who have experienced or witnessed a traumatic event.[43]10.SuperBetter SuperBetter is an app that helps users build resilience and achieve their goals by gamifying the process.It offers a variety of challenges and rewards to help users stay motivated and make progress.[44] 37] 4. Calm Calm is an app offering guided meditation and mindfulness exercises.It also offers other relaxation and sleep-aid features, such as sleep stories and ambient sounds.[38] 5. Shine Shine is an app that provides personalized daily inspiration and support.It uses AI to learn about users' needs and interests and then provides content and resources tailored to each user.[39] 6. DBT Coach DBT Coach is an app that provides users with tools and resources to help them practice dialectical behavior therapy (DBT), which teaches people how to manage their emotions, thoughts, and behaviors healthily.[40] 7. Companion CBT Companion is an app that helps users practice cognitive-behavioral therapy (CBT), which teaches people how to identify and change negative thought patterns and behaviors.[41] 8. MindShift CBT MindShift CBT is an app that helps users practice CBT techniques for anxiety and depression.1. Kintsugi Kintsugi utilizes facial and voice analysis to provide real-time emotional feedback to therapists, aiding in the early detection of emotional distress.[45] 2. IBM's Watson Health IBM's Watson Health employs AI to predict disease progression and treatment outcomes by analyzing comprehensive patient data.[46] 3. Cerebral Cerebral utilizes AI to support therapists in refining personalized treatment plans for patients with mental health conditions.[47] 4. Mindstrong Health Mindstrong Health employs AI to analyze smartphone keyboard interactions during teletherapy, providing therapists with insights into emotional states.[48] 5. Smartwatch Smartwatches equipped with AI algorithms monitor changes in sleep patterns, physical activity, and heart rate, offering valuable insights for mental health monitoring.[49] 6. Pear Therapeutics' reset Pear Therapeutics' reSET is an FDA-approved prescription digital therapeutic that tracks patient engagement and progress, enabling data-driven treatment adjustments.[50]