ARTIFICIAL INTELLIGENCE IN MENTAL HEALTH CARE: OPPORTUNITIES, CHALLENGES, AND ETHICAL DILEMMAS - Piotr Rzyczniok,; Mateusz Kopczyński,; Aneta Rasińska,; Justyna Matusik,; Justyna Jachimczak,; Paulina Bala, (International Journal of Innovative Technologies in Social Science, 2025)
Introduction and Objective: The increasing global burden of mental health disorders, exacerbated by the COVID-19 pandemic and the limitations of traditional mental health systems, has accelerated interest in digital health solutions. Artificial intelligence (AI) has emerged as a transformative force in mental health care, offering tools for diagnosis, intervention, and patient monitoring. This review aims to explore current applications, opportunities, and ethical challenges of AI-based tools in mental health, with an emphasis on responsible and equitable deployment.Review Methods: A narrative literature review was conducted using PubMed, Scopus, Web of Science, and Google Scholar. Peer-reviewed articles published between 2014 and 2022 were considered, with a focus on interdisciplinary sources covering clinical psychology, digital health technologies, AI development, and medical ethics. Key themes were synthesized across domains to provide a holistic understanding.State of Knowledge: AI technologies, including chatbots, machine learning algorithms, and predictive analytics, are increasingly integrated into mental health services. They offer scalable solutions for screening, personalized intervention, and early risk detection. However, concerns remain about algorithmic bias, privacy, transparency, and the digital divide. The current body of evidence supports AI’s potential to complement—rather than replace—human care, particularly when integrated responsibly within clinical frameworks.Conclusion: AI holds significant promise in improving access, personalization, and efficiency in mental health care. To harness its benefits, interdisciplinary collaboration, robust ethical oversight, and patient-centered design are essential. Further research is needed to evaluate long-term outcomes and ensure AI systems uphold clinical integrity, equity, and trust.