International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 12 | Dec 2024
p-ISSN: 2395-0072
www.irjet.net
AI IN MENTAL HEALTH DIAGNOSIS C R AJJAIAH1, ARUN RAMESH CHAVAN2, PAVAN RAJU KONKATI3, PRAVEEN4 1Bachelor of Engineering, Information Science and Engineering, Bapuji Institute of Engineering and Technology,
Karnataka, India
2Bachelor of Engineering, Information Science and Engineering, Bapuji Institute of Engineering and Technology,
Karnataka, India [3][4] ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Millions of people are impacted by mental health
approaches. By leveraging AI technology, mental health professionals can now provide personalized treatment plans based on individual patient data, leading to more effective and efficient care. Additionally, AI-driven tools have the potential to reduce stigma surrounding mental health by offering a more objective and data-driven approach to diagnosis and treatment.
issues every year, which has led to an increase in concern worldwide. The accuracy, consistency, and timeliness of traditional diagnostic techniques—which mostly rely on patient self-reporting and clinical evaluations—are frequently questioned. Recent developments in artificial intelligence (AI) provide a revolutionary method of diagnosing mental health issues by utilizing machine learning algorithms that can analyze large amounts of data, such as voice, physiological signals, and behavioral patterns. This study examines how AI might increase the precision, effectiveness, and scalability of mental health diagnosis. It does this by reviewing existing AI applications, their possible advantages, and related difficulties. Healthcare professionals can improve early detection and intervention tactics, resulting in more individualized therapy and improved patient outcomes, by utilizing AI. However, moral dilemmas and the requirement for strong validation models remain critical factors for future developments. The study concludes by discussing the potential for AI to complement human expertise in creating a more efficient and accessible mental health diagnostic framework.
1.1 Computational Psychometrics and Behavioral Analytics In the subject of psychometrics, which analyzes psychological traits, behaviors, and cognitive skills statistically, computational psychometrics applies machine learning and artificial intelligence approaches. Behavioral analytics, on the other hand, uses data-driven techniques to examine individuals' behaviors, responses, and patterns. By gleaning valuable insights from vast, intricate datasets that comprise behavioral, emotional, and cognitive markers, these domains work together to use AI to evaluate mental health.
1.2 Neurocomputational Modeling and Cognitive Diagnostics
Key Words: Mental Health Diagnosis, AI in Healthcare, Personalized Treatment, Healthcare Scalability, Data Analysis.
Neurocomputational modeling is the practice of simulating the brain's neural processes using computational methods. By building mathematical and algorithmic models that replicate brain activity, this method aids in the understanding of how neural circuits affect behavior, mental health, and cognition. Contrarily, cognitive diagnostics focuses on evaluating and analyzing cognitive processes, including memory, attention, reasoning, and decisionmaking, in order to identify deficiencies or diseases. Combining these two areas allows AI-driven neurocomputational models to replicate patterns of brain activity linked to particular illnesses, thereby diagnosing mental and cognitive health issues.
1. INTRODUCTION Global healthcare systems face a serious issue as the prevalence of mental health diseases rises. Conventional diagnostic techniques, which frequently depend on clinical observation and subjective reporting, can be laborious and inconsistent. However, the emergence of artificial intelligence (AI) presents a once-in-a-lifetime chance to transform mental health diagnosis. Large volumes of patient data might be analyzed by AI systems driven by machine learning algorithms, which could then identify minute patterns and correlations that could be missed by humans. This skill makes it possible to identify mental health issues earlier and with more accuracy, allowing for prompt interventions that can significantly enhance patient outcomes. In this study, we examine how AI-powered technologies are revolutionizing mental health diagnosis, improving accessibility, scalability, and precision while tackling some of the longstanding limitations of conventional
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2. Machine Learning Techniques in Mental Health Assessment Machine learning (ML) has emerged as a pivotal tool in mental health assessment, leveraging sophisticated algorithms to analyze complex datasets and uncover patterns that traditional methods may overlook. By facilitating the
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