Unified AI Approaches to Mental and Cardiovascular Health: Advancing Depression Detection, Predictive Analytics, and Ethical Integration

Abstract

This research paper explores the integration of artificial intelligence (AI) in the detection and management of mental health disorders, specifically depression, and cardiovascular diseases (CVD). It presents a comprehensive analysis of the VPSYC system, an AI-enhanced tool that leverages electroencephalography (EEG) data for real-time depression detection, providing clinicians with innovative solutions for timely intervention. Additionally, the paper discusses an AI-driven predictive modeling approach that analyzes patient data to assess cardiovascular risks, demonstrating the effectiveness of machine learning algorithms in generating accurate risk assessments.

The paper further examines the ethical implications associated with the deployment of AI in healthcare, emphasizing the importance of data privacy, transparency, and algorithmic fairness. It underscores the necessity for multidisciplinary collaboration to address the ethical challenges posed by AI technologies and to ensure that they enhance clinical practice without compromising patient rights.

Through a combination of literature review, methodology, and case studies, this research illustrates the potential of AI to transform mental health and cardiovascular care, ultimately aiming to improve patient outcomes through early detection and personalized treatment plans. The findings highlight the critical role that AI can play in the future of healthcare, alongside a call for responsible and ethical integration into clinical workflows.