ARTIFICIAL INTELLIGENCE IN PSYCHOLOGICAL DIAGNOSTICS: ADVANCEMENTS, ETHICAL CONSIDERATIONS, AND IMPLICATIONS FOR PERSONALISED MENTAL HEALTH TREATMENT

Abstract

Artificial Intelligence (AI) has become a pivotal in psychological diagnostics and treatment, offering a new paradigm for accuracy and efficiency. The integration of machine learning (ML), natural language processing (NLP), and facial recognition has allowed for more precise diagnoses and personalised treatments for various mental health disorders. AI’s ability to analyse large datasets, including speech patterns, behaviour, and physiological responses, enables early detection of conditions like depression, anxiety, PTSD, and schizophrenia. Recent studies have demonstrated how machine learning algorithms and NLP tools can enhance diagnostic accuracy by detecting emotional distress, thus offering non-invasive methods that complement traditional diagnostic techniques. Moreover, AI-driven platforms like Woebot have revolutionised therapy by delivering real-time personalised interventions based on user responses. However, despite these advancements, several ethical considerations must be addressed, such as data privacy, algorithmic bias, and the depersonalisation of care. This paper examines the role of AI in psychological diagnostics, explores recent innovations, and discusses the ethical challenges associated with its integration into mental health care. By balancing technological advancements with ethical responsibility, AI can offer substantial improvements in personalised mental health treatment and accessibility.