NOBLE APPROACH TO MINIMISE MENTAL HEALTH AND ILLNESS USING AI

 

Utkarsh Anand ¹, Vidyut Rajput ¹, Vipul Narayan ¹                 

 

¹ Galgotias University, Greater Noida, UP, India

 

1. INTRODUCTION

Artificial Intelligence (AI) is playing a pivotal role in transforming the healthcare sector, particularly in addressing mental health challenges. Historically stigmatized and often neglected, mental health is now acknowledged as a cornerstone of overall well-being. However, this increased recognition has also spotlighted the overwhelming scale of the mental health crisis, which continues to affect societies globally. The World Health Organization (WHO) reports that mental health disorders contribute significantly to the global disease burden, with depression being the leading cause of disability worldwide.

As the prevalence of mental health disorders rises, healthcare systems face unprecedented pressure, revealing the inadequacy of traditional approaches to mental healthcare. Conventional methods, which rely heavily on in-person consultations and therapies, are struggling to meet the growing demand for accessible, affordable, and scalable mental health solutions. This gap between demand and resources underscores the urgent need for innovative interventions.

In this context, AI emerges as a game-changing tool with the potential to revolutionize mental healthcare. By providing deeper insights into human behavior and emotions, AI offers solutions that were previously unattainable through conventional means. It enables innovative diagnostic methods, personalized treatments, and virtual therapy platforms, significantly expanding access to mental health services, reducing stigma, and improving patient outcomes. The integration of AI into mental healthcare represents a critical evolution, promising early intervention, greater accessibility, and tailored care.

Despite its potential, this shift also brings ethical concerns, regulatory hurdles, and the necessity for ongoing research and innovation. The growing burden of mental health disorders has reached epidemic proportions, accounting for approximately 16% of the global disease burden. Major conditions such as depression and anxiety cost the global economy an estimated $1 trillion annually in lost productivity, emphasizing the urgent need for effective solutions.

Deeply ingrained stigma further exacerbates the crisis, leaving many individuals without adequate care and perpetuating a cycle of neglect and suffering. Yet, AI offers hope. Its integration into mental health services presents a genuine opportunity to mitigate the crisis and transform the delivery of care. By facilitating early detection, personalized treatments, and innovative support systems, AI can redefine mental healthcare.

This narrative review is timely, as the rapid evolution of AI demands an assessment of its development and its intersection with mental health. By examining progress, future potential, and the challenges of integrating AI into mental healthcare, this review aims to highlight the imperative for adopting AI in addressing one of the most pressing health crises of our time Benrimoh et al. (2021), Kalmady et al. (2019).

 

2. LITERATURE REVIEW

The history of Artificial Intelligence (AI) in mental health care can be traced back to the early days of computing in the mid-20th century. During this period, scientists began envisioning the possibility of robots mimicking cognitive activities, paving the way for significant advancements in the field. Pioneering researchers Allen Newell and Herbert A. Simon spearheaded groundbreaking studies in the 1950s and 1960s, developing AI models of human problem-solving. Their work introduced fundamental concepts of symbolic AI, which later played a crucial role in simulating cognitive processes relevant to mental health.

Although these early efforts were primitive by today’s standards, they laid the groundwork for an important intersection between psychology and AI. By the late 1960s and early 1970s, one of the first AI applications in psychology emerged with the development of ELIZA, a chatbot created by Joseph Weizenbaum. ELIZA adopted the persona of a Rogerian psychotherapist, engaging users in text-based conversations. While its responses were simple and limited, ELIZA demonstrated how technology could facilitate mental health discussions, sparking interest in the potential of AI in this domain.

Since then, the use of AI in mental health care has steadily   evolved. The 1980s saw the advent of expert systems, which were rule-based AI technologies designed to emulate human expertise. These systems offered diagnostic and treatment recommendations across various psychological domains. Although the capabilities of early AI systems were relatively limited compared to modern advancements, they marked a significant step in integrating technology with mental health care Price et al. (2020), Torous et al. (2018).

 

3. METHODOLOGY

The objective of this paper is to adopt a narrative review approach to examine the role of Artificial Intelligence (AI) in mental health. For this study, we selected papers published in reputable peer-reviewed journals, conference proceedings, and recognized online databases that specifically address the application of AI in mental health. Additionally, we included review articles that summarize, analyze, or contextualize significant portions of existing literature. Papers that failed to meet these criteria—such as duplicates, non-English publications, or articles unrelated to the topic—were excluded.

The screening process involved three stages: an initial review of titles, followed by abstracts, and finally a full-text review to ensure all selected papers met the inclusion criteria. The literature search focused on identifying publications related to "Artificial Intelligence in Mental Healthcare," which were published in academic journals, conference proceedings, and trusted online sources between January 2019 and December 2023. The selected papers were analyzed to extract key insights, identify emerging trends, provide practical examples, and address the ethical considerations surrounding AI in mental health.

The Past: Preliminary Applications of Artificial Intelligence in Mental Healthcare

Despite a growing body of research, the application of AI in mental healthcare remains largely conceptual. Many studies are focused on proof-of-concept approaches using small sample sizes, with limited exploration of clinical validity or generalizability—common challenges when adapting AI to a new field. Two primary applications have been extensively studied: disease diagnosis and treatment response prediction.

In terms of diagnosis, early detection of mental illnesses is critical for improving mental health outcomes and deepening our understanding of these conditions. AI has demonstrated potential in clinical decision support by identifying aspects of mental illness that might be overlooked by clinicians. The complexity and multidimensionality of many neuropsychiatric conditions often hinder comprehensive diagnostics, creating an opportunity for data-driven, computational approaches.

As noted by Graham et al., supervised learning and natural language processing (NLP) are the most commonly employed techniques in mental health-related AI research, with depression being the most frequently studied condition. Among various AI and machine learning methods applied in this field, deep learning appears to hold the greatest promise.

For example, Su et al. report an increasing number of studies using deep learning models to study mental health outcomes, with some developing promising disease risk prediction models based on both clinical and non-clinical data. Similarly, In Jacobset al. (2020) review of studies on depression treatment response prediction using deep learning found encouraging accuracy rates of approximately 80%. However, these studies often have limitations, such as small sample sizes and challenges in interpreting the results.

Most explored include disease diagnosis and treatment response prediction.In terms of diagnosis, early detection of mental illnesses is important to improve quality in mental health and to understand more about mental illnesses. AI has shown potential in mental health clinical.

Current research trends are shifting toward uncovering the pathophysiology of mental illnesses by identifying associations with objective indicators, such as biomarkers. For instance, Kalmady et al. demonstrated a biologically informed diagnostic approach by accurately classifying schizophrenia using fMRI images. Their ensemble model, which mimicked brain connectivity structures, achieved 87% accuracy with a dataset of 174 subjects. Similarly, Sharma and Verbeke explored associations between biomarkers and four anxiety disorders using machine learning techniques. Their study revealed relationships between biomarkers and anxiety disorders; however, the dataset was limited to Dutch citizens, restricting generalizability. The authors emphasized the need for future studies to use larger, more diverse datasets, validate findings clinically, and expand biomarker selection to increase applicability Lipschitz et al. (2019), Mall et al. (2024). decision-support, capturing aspects of mental illness that            

 

3.1. The present: building larger and more heterogeneous data sets

Although many studies achieve high accuracy scores, two critical limitations significantly impact current research on AI applications in mental healthcare:

 

1)    Limited and Homogeneous Data Sets: High-quality datasets that include diverse mental health phenotypes are scarce. Existing clinical psychiatric data often rely on symptom-based diagnostic frameworks rooted in social constructs, such as the Diagnostic and Statistical Manual of Mental Disorders (DSM-V). However, these frameworks may not serve as reliable predictors of mental illnesses.

2)    Symptom      Heterogeneity and Subtyping: The a clinician may not notice. The heterogeneity and multidimensionality of so many neuropsychiatric conditions frustrate the development of comprehensive diagnostics and motivate data-driven approaches that leverage computation.In Mall et al. (2023) note in their review of AI in mental healthcare, supervised learning and NLP are by far the most common techniques applied in mental health studies. The most studied condition was depression.Among all the AI/ML methods applied to mental health so far, deep learning has probably shown the greatest potential. In Narayan and Daniel (2021) note that an increasing number of studies are using deep learning models for studying mental health outcomes, with several studies developing promising disease risk prediction models using both clinical and non-clinical data. In Strączkiewicz et al. (2021) review of studies using deep learning in the prediction of depression treatment response found promising accuracies around 80%; however, weaknesses include small sample sizes and results that may be difficult to interpret.

Variability of symptoms within mental health conditions complicates diagnosis and necessitates further stratification into disease subtypes, which must then be linked to biological phenotypes. For example, Drysdale et al. investigated depression, a condition with profound heterogeneity, by clustering patients into subtypes based on a multisite fMRI dataset of 1,188 patients. They identified four connectivity-based subtypes of depression, laying the groundwork for biologically informed subtyping.

A promising approach to addressing these challenges involves multimodal data integration, which combines various data types to better reflect the complexity of neurological disorders. Rahaman et al., for example, developed a deep learning framework that fused neuroimaging and genomic data. This framework captured interactions between latent features and evaluated their complementary roles in characterizing schizophrenia. Their model achieved 88% accuracy on a dataset of 437 subjects, demonstrating the potential of heterogeneous data integration. Larger, multisite datasets are expected to further enhance generalizability.

In the context of clinical decision support, Benrimoh et al. proposed a deep learning system to help select effective depression treatments. Their work highlighted the necessity of training models on large, diverse datasets sourced from multiple groups and institutions. Additionally, the study underscored practical considerations for implementing such systems in real- world settings.

Recent global events have spurred interest in generating and integrating datasets at a global level, offering new opportunities to address the limitations of small and homogeneous data sources. These advancements could help bridge gaps in understanding and managing the complexity of mental health conditions while paving the way for more personalized, effective interventions.

 

4. FUTURE WORK

Digital Health and Digital Psychiatry in Mental Healthcare

Digital health has emerged as a promising avenue for biomedical data generation, gaining significant traction across the medical field in recent years. The COVID-19 pandemic accelerated its adoption, particularly in mental healthcare, where it is referred to as digital psychiatry. This subfield is especially relevant because it significantly enhances access to care. Even before the pandemic, Hariman et al. predicted that although no direct evidence yet existed, rapid technological advancements would eventually revolutionize mental healthcare Narayan et al. (2023), Kumar et al. (2022).

To prepare for this transformation, they recommended integrating technology training into medical and psychiatric curricula, organizing expert panel symposia on the use of technology in mental health practice, and conducting ongoing clinical trials in collaboration with technology companies to test new interventions. Furthermore, they advocated for the development of guidelines addressing critical issues such as data privacy and called for the establishment of an International Committee on Digital Psychiatry, inviting global psychiatry associations to participate.

The growing interest in digital psychiatry is evident in current literature. For example, In Narayan et al. (2023) recently highlighted the expanding body of studies exploring the use of smartphones, wearable devices, social media platforms, virtual reality, and chatbots in mental healthcare.

 

4.1. Improving Diagnoses with AI

Digital psychiatry also holds potential to enhance diagnostic accuracy by identifying patterns that might elude clinicians. A pioneering study by Narayan et al. (2017) employed naturalistic, passively collected movement data to improve understanding of behavioral phenotypes associated with psychiatric disorders. Using cluster analysis, they identified distinct patterns in actigraphy data that corresponded to depression and schizophrenia.

Their study involved analyzing raw, unlabeled minute- level actigraphy data collected over a week from three groups: individuals with schizophrenia, individuals with depression, and a control group. The researchers successfully predicted discriminative physical activity patterns representative of these psychiatric conditions. While this study demonstrated the feasibility of pattern discovery to understand motor differences in psychiatric disorders, the authors acknowledged several limitations. Chief among these was the small sample size, which was drawn from a single institution, limiting the study's generalizability.

 

4.2. Enhancing Treatment Adherence

Digital psychiatry offers significant potential for improving treatment adherence through innovative monitoring solutions. In a longitudinal pilot study, Straczkiewicz et al. demonstrated the feasibility of combining smartphone-based digital phenotyping with digital medication data to monitor psychotropic medication adherence among patients with severe mental illness (SMI).

Additionally, digital psychiatry shows promise for public health surveillance on a global scale. For instance, a study on first-episode psychosis conducted in both the United States and India highlighted the feasibility and acceptability of using a smartphone app to monitor symptoms, perform cognitive assessments, and collect digital phenotyping data.

 

4.3. Digital Interventions Powered by AI

AI-driven digital health interventions have the capacity to reach a broader population, offering scalable solutions to mental health challenges. For example, two trials conducted by Araya et al. demonstrated the effectiveness of a smartphone-delivered intervention. Over a six-week period, the intervention yielded significant improvements at the three-month follow-up in cohorts of individuals managing diabetes and/or hypertension.

However, it is important to note that the application of digital interventions in mental health is still in its early stages and continues to evolve. Extensive clinical trials are needed to establish their efficacy and clinical significance. A systematic review by Garrido et al. found that while digital interventions provide tangible benefits, they are most effective when their use is closely supervised.

The authors emphasized the importance of improving patient engagement to maximize the benefits of digital health interventions. Many ongoing trials are exploring increasingly complex digital solutions for patients with a diverse range of psychiatric conditions, aiming to refine their effectiveness and expand their applications.

 

5. LIMITATIONS

However, many studies in digital psychiatry face significant limitations, such as small sample sizes that restrict their generalizability. Larger, more diverse trials are essential to establish the clinical significance of digital interventions. Additionally, a lack of standardization in digital psychiatry poses challenges. A recent review by emphasized that engagement with digital mental health interventions is often inconsistent and underreported due to the absence of standardized reporting practices. The authors recommend adopting a set of reporting guidelines that would ensure the inclusion of essential information when documenting randomized controlled trials.

·        Ethical Considerations

As mental healthcare increasingly adopts larger datasets and advanced AI models, its integration of these technologies into clinical practice has lagged behind other fields. This slower adoption stems from concerns about safety and trustworthiness. Cearns et al. highlighted that psychiatry has traditionally relied on statistical inference rather than prediction, which creates significant barriers when translating machine learning methodologies into practice. While machine learning is well-suited to psychiatric applications, studies report varying levels of accuracy, leading to doubts about the reliability of AI-generated predictions.

 

·        Trustworthiness of AI

Another critical concern is the trustworthiness of AI clinical decision-support tools, which can occasionally produce inaccurate recommendations that adversely affect clinician decision-making. Jacobs et al. demonstrated this in a study involving 220 clinicians. Participants were presented with patient scenarios, some of which included machine learning-generated treatment recommendations and explanations. The study found that interacting with these AI recommendations did not significantly improve treatment accuracy compared to independent decision-making by clinicians.

More concerningly, reliance on incorrect AI recommendations often reduced decision-making accuracy. Furthermore, the explanations provided for these recommendations failed to prevent clinicians from overrelying on flawed AI outputs. These findings underscore the need for rigorous validation of AI systems and the development of safeguards to enhance the reliability and trustworthiness of machine learning applications in psychiatry.

 

CONFLICT OF INTERESTS

None. 

 

ACKNOWLEDGMENTS

None.

 

REFERENCES

Benrimoh, D., et al. (2021). A Machine Learning Framework for Treatment Selection in Depression. Journal of Affective Disorders, 279, 345-353.

Jacobs, M., et al. (2020). The Influence of AI Recommendations on Clinical Decision-Making. Journal of Medical Internet Research, 22 (5), e16315.

Kalmady, S. V., et al. (2019). Identifying Schizophrenia using Structural Neuroimaging Data: A Machine Learning Approach. Frontiers in Psychiatry, 10, 1-9.

Kumar, V., et al. (2022). A Machine Learning Approach for Predicting onset and Progression Towards Early Detection of Chronic Diseases. Journal of Pharmaceutical Negative Results, 6195-6202.

Lipschitz, J. M., et al. (2019). Engagement in Digital Mental Health Interventions: A Systematic Review. Psychiatric Services, 70 (5), 441-449.

Mall, P. K. et al. (2023). FuzzyNet-Based Modelling Smart Traffic System in Smart Cities using Deep Learning Models.  Smart Cities and Intelligent Systems, 15 (3), 450-472.

Mall, P. K., et al. (2024). Optimizing Heart Attack Prediction Through OHE2LM: A Hybrid Modelling Strategy. Journal of Electrical Systems, 20 (1). https://doi.org/10.52783/jes.665 

Narayan, V. & Daniel, A. K. (2021) CHHP: Coverage Optimization and Hole Healing Protocol using Sleep and Wake-Up Concept for Wireless Sensor Network. . International Journal of Advanced Networking, 12 (2), 367-390.

Narayan, V., et al. (2017). E-Commerce Recommendation Method Based on Collaborative Filtering Technology. International Journal of Current Engineering and Technology, 7 (3), 974-982.

Narayan, V., et al. (2023). A Comprehensive Review of Various Approaches for Medical Image Segmentation and Disease Prediction. Wireless Personal Communications, 132 (3), 1819-1848. https://doi.org/10.1007/s11277-023-10682-z

Narayan, V., et al. (2023). Extracting Business Methodology: Using Artificial Intelligence-Based Method. Semantic Intelligent Computing and Applications, 16, 123. https://doi.org/10.1515/9783110781663-007

Price, M., et al. (2020). Patterns in Actigraphy Data Reveal Behavioral Phenotypes in Psychiatric Disorders. Journal of Psychiatric Research, 128, 15-25.

Strączkiewicz, M., et al. (2021). Quantifying Medication Adherence with Smartphone Digital Phenotyping. Journal of Medical Systems, 45 (8), 1-12.

Torous, J., et al. (2018). Digital Psychiatry in the Smartphone Era: A Comprehensive Review. Current Psychiatry Reports, 20 (9), 1-12.