

<strong>Paper Title</strong><br>

AI-DRIVEN INTEGRATED HEALTH SCREENING AND PREVENTIVE INTERVENTION FRAMEWORK FOR EARLY DETECTION, RISK PREDICTION, AND HOLISTIC WELL-BEING ENHANCEMENT AMONG INFORMATION TECHNOLOGY EMPLOYEES<br>

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<strong>Abstract</strong><br>

The rapid growth of the information technology (IT) sector has introduced significant occupational health challenges, including musculoskeletal disorders, digital eye strain, sedentary behavior, technostress, burnout, anxiety, and sleep disturbances. The increasing integration of artificial intelligence (AI) into workplaces has further intensified concerns regarding job insecurity and employee well-being. Existing workplace wellness programs are often fragmented and reactive, limiting their effectiveness in addressing the interconnected physical, psychological, and behavioral health needs of IT professionals. This study proposes an AI-driven integrated health screening and preventive intervention framework specifically designed for the IT workforce. The conceptual model is based on the Job Demands–Resources (JD-R) Model, Biopsychosocial Model, Health Belief Model (HBM), and Technology Acceptance Model (TAM). The framework combines AI-powered computer vision, wearable sensors, predictive analytics, mobile health applications, digital mental health support, and workplace wellness interventions to facilitate continuous health monitoring and early risk detection. By analyzing real-time physiological and behavioral data, the system aims to identify early indicators of stress, burnout, cardiovascular risk, poor ergonomics, and sedentary lifestyles before they develop into chronic conditions. The framework promotes personalized interventions, ergonomic guidance, and healthy work practices, ultimately enhancing employee well-being, productivity, and organizational sustainability while advancing the field of AI-enabled occupational healthcare.

Keywords - Artificial Intelligence, Occupational Health, IT Employees, Preventive Healthcare, Technostress, Wearable Sensors, Workplace Wellness, Predictive Analytics, Mental Health, Ergonomics.