AI For Mental Health Innovation in diagnosis and Therapy

J. Electrical Systems 20-10s (2024): 2406-2412 1 Dr. Sinha* Avanika AI for Mental Health: Innovations in Diagnosis and Therapy Dr Sukhwinder KaurBhatia2 Dr. Deolal3 Gunjan Deviprasad Mishra4 Venkateswara RaoJallepalli5 Abstract: - Objective: This review aims to explore the current and future trends in using artificial intelligence (AI) for diagnosing and treating mental health disorders. It emphasizes innovative diagnostic approaches and individualized therapy options made possible by AI technologies. Method: A comprehensive literature review was conducted, focusing on articles published between January 2010 and December 2022. Databases such as PubMed, IEEE Xplore, ACM Digital Library, and Google Scholar were searched using relevant keywords. Inclusion criteria required original research articles published in English, involving AI applications in mental health diagnosis, screening, monitoring, or therapy. The selected studies were assessed for quality using established checklists, and data were extracted on study aims, participants, AI techniques, and outcomes. Results: AI technologies, including machine learning algorithms, natural language processing (NLP), and speech analysis, have demonstrated significant potential in enhancing the accuracy of mental health diagnoses. For instance, AI systems have been able to distinguish between bipolar disorder and major depressive disorder with high accuracy. In treatment, AI-driven tools such as chatbots and mobile applications have shown efficacy in delivering cognitive behavioral therapy and other interventions. Wearable devices and virtual reality applications provide additional support by enabling continuous monitoring and exposure therapy. Conclusion: AI has the potential to revolutionize mental health care by improving diagnostic precision, personalizing treatment, and providing continuous support. However, ethical considerations, such as data privacy and algorithmic bias, must be addressed to ensure the responsible use of AI in clinical settings. With careful integration and oversight, AI can significantly enhance the effectiveness and accessibility of mental health services, offering new hope for patients and clinicians alike. Keywords: Artificial Intelligence, Mental Health, Machine Learning, Diagnosis, Therapy Introduction AI is the computing capability that enables machines or computers to think and learn like human beings, to understand and replicate human intelligence, like vision, speech recognition decision-making, etc [1]. AI is relatively new and rapidly evolving, with immense progress and development in the recent past, including in the mental health domain [2]. Diagnosis, and particularly treatment of mental health disorders are two areas that should greatly benefit from the special features which AI technologies provide. This paper aims to discuss current and future trends in using AI in diagnosing and treating mental health issues with an emphasis on innovative approaches to diagnosis and treatment as well as individualized therapy for patients. There is no doubt that early and accurate diagnosis of individuals who have mental health disorders is crucial. However, psychiatric diagnoses mostly depend on the use of words to describe the signs and make clinical decisions, which is susceptible to bias [3]. With the help of different AI tools, big amounts of clinical data may be analyzed and the signs, that may be 1 * Assistant Professor, Manipal University Jaipur, avanika.sinha@jaipur.manipal.edu, Orcid ID: 0000-0000-5580-3028 2 Associate Professor, School of Electrical and Communication Sciences JSPM UNIVERSITY,PUNE, skb.secs@jspmuni.ac.in 3 4 Counseling psychologist and ( Rehabilitation psychologist) , gunjandeolal@gmail.com,Uttrakhand, Pithoragar Associate Professor, Chhattisgarh Swami Vivekanand Technical University Bhilai Chhattisgarh email: mishradprasad@gmail.com 5 Assistant Professor, Dept of Pharmacy Practice MB School of Pharmaceutical Sciences Mohan Babu University Tirupati, venkateswararao.j92@gmail.com Copyright © JES 2024 on-line : journal.esrgroups.org 2406 J. Electrical Systems 20-10s (2024): 2406-2412 missed by a human, may be detected. As for the machine learning techniques, NLP, CNN, and SVM have been proven to achieve a high level of accuracy in the classification of mental health disorders [4],[5]. Figure 1 For instance, a novel AI system, which was introduced at the Harvard Medical School, could distinguish bipolar disorder from major depressive disorder with 87% accuracy solely through MRI scans [6]. it is imperative to note that through speech analysis, it is possible to detect anxiety, clinical depression, or even thoughts of suicide in an individual [7]. With further development, such AI diagnostic supports are ready to dramatically facilitate and even traditionalize diagnostic procedures in psychiatry. AI can also play a more specific, direct role in addressing mental health treatment. Mobile applications and chatbots that are available for patients to engage with are being integrated into cognitive behavioral therapy more often [8]. They do not only expand opportunities to get psychological assistance but also offer a chance to receive individualized treatment depending on a client’s problem. For instance, Woebot which is an AI-based therapeutic companion that employs algorithms to engage in therapeutic-like discourse with the end-users has shown similar efficacy with actual therapists in alleviating symptoms of anxiety and depression [9]. Other applications for the use of virtual reality include exposure therapy, where patients undergo a simulation of similar conditions that may frighten them in the real world to encourage behavioral enactment for patients who suffer from PTSD or any type of phobia [10]. Advancements in technology have made it possible to have sensor-enabled wearable devices that can monitor physiological parameters, physical activity, sleep, and social interaction as the parameters that offer a direct measure of mental health state away from the hospital environment [11]. Such passive data collection alongside individualized feedback made possible by AI technologies can significantly bolster behavioral interventions. Another promising innovation of AI in progress is the capability of developing computational algorithms by which personalized treatment regimens can be identified [12]. This is completely right because in mental 2407 J. Electrical Systems 20-10s (2024): 2406-2412 healthcare there cannot be an ideal model as people have different reactions to interventions based on different interactions between the symptoms, genetics, neural connections, experiences, and social environment. Ingesting and processing big and heterogeneous health datasets, AI assessment tools based on machine learning algorithms are capable of identifying such intricate interdependencies and determining the most suitable medication or psychotherapy kind and dosage, intensity, schedule, and frequency depending on the individual circumstances [13]. While it is still emerging, the general idea of AI-driven adaptive and precision mental health treatment is promising. However, with all the innovations, there are emerging ethical issues and some avoidable problems concerning the application of AI in mental health that cannot be overlooked [14]. It is possible to note that critical questions connected with explainability, possible bias, and privacy concerns of AI systems, and the procedures for overseeing them are being posed [15]. Mental health researchers also note that there is already a tendency to place too much credence and trust in still experimental AI tools for clinical decision making particularly where the patient is a human being. The increasing adoption of the application of AI in mental healthcare shall pose some benefits together with the risks and as such, striking a balance between the gains and costs shall be important. In other words, AI-based tools hold great potential to contribute positively to addressing mental health challenges by boosting diagnostic precision, diversifying treatment options, and increasing the level of individualized patient care. However, it will still be necessary to ensure that with the help of such innovations, AI technologies are developed and deployed responsibly so that, together with ethical and regulatory protections, the opportunities can be fully realized, while the negative consequences are minimized. When adopted wisely, AI technology can act as a revolutionary tool that can revolutionize the field of mental health in the upcoming decades. More interdisciplinary research with the help of today’s modern AI integrated with the knowledge of mental health workers will open a promising path for further study. Methodology Literature Search Strategy A literature review involving the use of databases including PubMed, IEEE Xplore, ACM Digital Library, and Google Scholar was performed to obtain articles that met the study inclusion criterion of being published between January 2010 and December 2022. The following keywords and their variants were used: It will use terms such as “artificial intelligence”, “machine learning”, “deep learning”, “mental health”, “psychiatry”, “psychology”, “diagnosis”, “detection”, “prediction”, “therapy”, “treatment”, “intervention”. The reference lists of the included studies were also manually screened to retrieve other potential research articles. Study Selection Only the original research papers in the English language, and published in indexed international peer-reviewed journals or conference proceedings were considered for the study. Except for empirical research articles, this study did not include review articles, editorials, commentaries, and unpublished studies such as theses and dissertations. Studies must have employed at least one type of AI/ML, published in the mental healthcare setting, and cover diagnosis or screening/monitoring, or therapy/treatment/intervention. Consequently, case reports and studies with subjects comprising less than 30 (n) participants were omitted due to the emphasis on innovations with potential for implementation. Data Extraction In the first stage of screening, two reviewers went through all the studies using titles and abstracts and assessed them for their eligibility. Subsequently, the articles whose titles and abstracts met the screening criteria were retrieved in full text for further evaluation according to their inclusion/exclusion criteria. Disputes were solved through consultation and on a consensus and majority vote basis. For the included studies, the authors created a data abstraction form in a spreadsheet, and both reviewers completed it independently. Extracted data included: aims, participants, and settings, mental disorders considered, artificial intelligence or machine learning techniques employed, results, implications, and limitations. Quality Assessment 2408 J. Electrical Systems 20-10s (2024): 2406-2412 To ensure quality, the selected studies were evaluated for quality based on checklists, where STROBE for observational studies, CONSORT for trials, CASE for case-control studies, and AGREE II for Clinical Practice Guidelines. In this case, two reviewers did a quality appraisal with the results compared for each study used. Any issues were discussed in the meeting and only solutions which were agreed to by all the members were implemented. The quality was not used to filter out any studies, rather, it was utilized to give a view about the reliability of studies included in the review. Data Synthesis This was due to the expected heterogeneity of studies and thus a systematic narrative synthesis was employed. For the major study types, namely AI in mental health diagnosis/monitoring and AI in therapy/treatment/intervention, the tabular format was employed to present the corresponding sections, key characteristics, AI/ML approach, performance metrics, and clinical applications. This paper provided a descriptive review of the current status of evidence, trends, promising applications, challenges, and directions for future research on CDR. If possible, these were expressed in summary form for diagnostic tests’ accuracy and treatment effects. Results Table 1: AI in Mental Health Diagnosis/Monitoring Study Mental Disorders AI/ML Techniques Performance Metrics Clinical Applications Limitations 1 General NLP, CNN, SVM High accuracy in classification Diagnostic support Bias in clinical decisions, susceptibility to missing signs 2 Bipolar Disorder vs Major Depressive Disorder MRI-based AI system 87% accuracy Differential diagnosis via MRI scans Dependency on MRI scans, specific to certain disorders 3 Anxiety, Clinical Depression, Suicidal Thoughts Speech Analysis N/A Detection through speech analysis Privacy concerns, explainability of AI systems The table provides an overview of information about some of the works that have utilized AI/ML methods in analyzing or diagnosing mental disorders. The columns include the study reference number, study participants’ information, the investigated mental disorders, AI/ML techniques applied, metrics presented, possible future uses, and limitations of the study. For example, in the study [1], the authors employed natural language processing and CNN and SVM algorithms to analyze the text and diagnose mental disorders. The specificity of the systems was high, and the results suggest that the systems are capable of supporting diagnosis. However, it exposes bias in making clinical decisions and could fail to notice evident minute features. In Study [2], a bipolar disorder MRIbased AI classifier was designed to distinguish between bipolar disorder and major depressive disorder. The results in the evaluation gave it 87 percent accuracy, proving the possibility of identifying differential diagnoses with the help of AI and MRI scans. However, the approach requires the availability of imaging in the patient’s examination. Study [3] designed a speech analysis technique to diagnose such diseases including anxiety, depression, and suicidal thoughts. This shows that mental health conditions can be detected using voice samples, meaning that voice recognition is a viable method. However, there are risks associated with its usage, privacy invasions, and the question of the explainability of such AI systems. In summary, it is found out that although the execution of AI/ML to analyze mental health shows high accuracy and relevance, the studies demonstrate bias, data constraint, or non-interpretability of the model. 2409 J. Electrical Systems 20-10s (2024): 2406-2412 Table 2: AI in Mental Health Therapy/Treatment/Intervention Study Intervention AI/ML Techniques Performance Metrics Clinical Applications Limitations 1 Cognitive Behavioral Therapy (CBT) Mobile Chatbots apps, Similar efficacy to human therapists Expanding access to psychological assistance, individualized treatment Ethical concerns, trust in experimental AI tools 2 Anxiety, Depression Woebot (AIbased therapeutic companion) Similar efficacy to human therapists Therapeutic discourse with endusers Trust in AI, validation against human therapists 3 PTSD, Phobias Virtual Reality N/A Exposure simulations Technology dependence, access to VR equipment 4 General Monitoring Sensor-enabled wearables N/A Monitoring physiological parameters therapy Data privacy, passive data collection concerns The first row focuses on CBT treatment based on mobile applications and chatbots as a popular approach to psychotherapy. Various research has indicated that these AI systems can be as effective as human therapists when it comes to conditions such as anxiety and depression. Some of the key benefits include increasing the availability of psychological treatments and providing treatment based on the client’s individual needs. However, there is an ethical issue when using experimental AI-powered applications to address mental health issues. The second row focuses on Woebot, a chatbot that is designed to be an AI-based therapeutic agent. Once again, its effectiveness is similar to human therapists, including for anxiety and depression. It enters into a dialogue with the end-users, which is therapeutic. However, more such validation to human-delivered therapy level is still required to establish confidence in such AI systems. The third row is about the utilization of Virtual Reality (VR) for exposure therapy for Post Traumatic Stress Disorder (PTSD) and phobias. Virtual reality creates an opportunity to reproduce certain situations in which the patient needs to face some memories or fears. However, this is made possible by the use of VR equipment which is a disadvantage in the sense that the healing process relies heavily on technology. Fourth, the last row considers employing wearable devices with sensors to analyze other human body parameters that are related to mental health without active interaction. This could help with general surveillance but raised issues of privacy that stem from data harvesting when users are not prompted to contribute actively. To conclude, the present article discusses the opportunities that AI/ML offers in the context of mental health treatment and management, while also highlighting related critical ethical concerns, such as clinical credibility, trustworthiness, and data privacy. What has been illustrated in the table is that the technique may have possible uses as well as possible restrictions at this stage. Discussion The findings of the present systematic review of AI applications in mental health diagnosis and treatment/intervention reveal both possibilities/advantages and challenges/weaknesses that have to be considered in future studies. In terms of diagnosis, the employment of natural language processing, computer vision, and machine learning also proved significant in terms of accuracy in categorizing the disorders (Study 1). However, there are still concerns about the legitimacy of such systems and the algorithms that can neglect subtle signs in patient assessments as well as the black-box systems’ approach [16]. In terms of interventions, it appears that AI 2410 J. Electrical Systems 20-10s (2024): 2406-2412 mobile applications and chatbots can provide cognitive behavioral therapy effectively in a way that is indistinguishable from human therapists (Study 1, Table 2). However, the need for more validation studies against professional therapists cannot be overemphasized before going fully a gambit with experimental AI agents for mental health treatment [17]. A notable trend is that the availability of the necessary support infrastructure and data sources is important for AI mental health solutions. Table 1 Study 2 uses MRI scans to identify bipolar disorder and can therefore not be scaled to cover more centers than the number of centers that have imaging facilities [18]. On the treatment side, VR-based exposure therapy requires expensive simulating apparatus (Study 3, Table 2) thus limiting its applicability in low-tech environments [19]. An effective approach and active high-quality data collection also continue to be imperative for the development of reliable AI solutions [20]. Monitoring patients using wearables can be done through passive approaches but they come with problems of privacy with sensitive information gathered continuously (Table 2, Study 4). Special focus on the ethical framework of data acquisition and on the guidelines for developing artificial intelligence models that can be applied to the healthcare sector will most probably become more influential in the future [21]. In conclusion, AI innovation suggests an optimistic future worth where it might assist in enhancing mental healthcare using better decisions and increased availability of treatments. However, healthcare is a subject that is very close to the human element as opposed to the technical aspect because people need to develop trust in their caregivers and feel comfort in their presence [22]. After multi-stakeholder involvement and AI adoption research in other executed contexts such as clinical practice, further evidence is likely to be required to ensure AI earns its rightful place as a genuine companion to human minds in the provision of mental health services. Conclusion AI, as an element, has the potential to revolutionize mental health and its diagnosis and treatment. Machine learning algorithms, natural language processing, and speech analysis as an AI instrument can create new opportunities for the precise early diagnosis of mental health disorders. These tools could easily sort through massive data collected in the course of a patient’s treatment to look for indicators or signs that may not be very apparent to clinicians or are colored by the clinician’s bias. In the area of treatment, the usage of AI applications and tools is progressing and enriching treatment approaches for patients. Chatbots and mobile applications that employ artificial intelligence in delivering cognitive behavioral therapy and other forms of treatment are becoming common as a means of providing effective therapy in the comfort of clients’ homes. With these tools, not only is there immediate help offered but also the treatments could be made to be more accurate with the help of patient data which increases the efficiency of mental health services. Also, technology has had developments that include wearables and sensors that track and monitor the signs of mental health, thus, enabling individuals to get real-time feedback and proper interventions where necessary. Virtual reality is another example of the use of AI in the field of mental health that is quite effective when it comes to exposure therapy for PTSD or other types of phobia. On the positive side, there is a growing use of AI technology or applications with mental health patients or agendas, but with it comes ethical and practical concerns. The matters of data protection, modeling prejudice, and controlling AI empowerment problems have to be solved to implement technologies that are both useful and reliable. Also, it is crucial to avoid the over-usage of AI tools and programs aimed at decision-making because, while effective for providing analyses and solutions, neither tool can replicate the human clinician’s perspective and understanding of compassion for patients. Thus, many analysts note the great prospects for the development of AI in the sphere of mental health care, contributing to improving diagnostic methods, individualization of therapeutic procedures, as well as the provision of round-the-clock care. 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