ARTIFICIAL INTELLIGENCE IN MENTAL HEALTH DIAGNOSTICS: ACCURACY, ETHICS, AND CLINICAL IMPLICATIONS

Abstract

This study examines how Artificial Intelligence can be used to diagnose mental health disorders, focusing on diagnostic accuracy, ethical concerns, and effects on clinical practice in Karachi, Pakistan, where mental health challenges are intensified by under diagnosis, subjectivity, and a shortage of trained professionals in low-resource settings. The study quantitatively evaluates the performance of AI-based diagnostic tools, including machine learning and natural language processing models, in comparison with traditional clinician-based diagnostic methods, while also assessing ethical issues such as data privacy, bias, and transparency and their perceived impact on clinicians and patients. Data were collected through standardized diagnostic questionnaires and clinical interviews from 500 patients and 200 healthy controls. The findings show that AI-based applications outperform conventional diagnostic tools in terms of accuracy and speed; however, they also raise significant ethical concerns. The study concludes that Artificial Intelligence has strong potential to transform mental health diagnostics, provided that ethical considerations and effective implementation strategies are prioritized, particularly in resource-constrained settings like Karachi, Pakistan.