Aug 2026· Adolescência e Saúde· 0 citations· 10 references
TL;DR
AI should be regarded as a supportive technology rather than a replacement for professional nursing judgment or human therapeutic relationships, particularly regarding privacy, confidentiality, informed consent, algorithmic bias, transparency, accountability, patient safety, therapeutic relationships, and the risk of over-reliance on automated systems.
Abstract
Artificial Intelligence (AI) is rapidly transforming healthcare by supporting clinical decision-making, patient monitoring, documentation, education, research, and personalized care. Mental health nursing is an important area in which AI has the potential to improve early identification of mental health problems, continuous monitoring, therapeutic support, risk assessment, and access to care. Recent advances in machine learning, natural language processing, predictive analytics, conversational agents, and generative AI have expanded the possibilities for supporting individuals experiencing depression, anxiety, psychosis, substance-use disorders, and other mental health conditions. Evidence suggests that AI-based systems can assist in detecting symptoms, predicting clinical risks, monitoring changes in behaviour and mood, and providing accessible digital interventions. However, the use of AI in mental health also creates significant ethical and professional concerns, particularly regarding privacy, confidentiality, informed consent, algorithmic bias, transparency, accountability, patient safety, therapeutic relationships, and the risk of over-reliance on automated systems. Mental health nurses are uniquely positioned to ensure that AI remains person-centred and clinically appropriate because they combine continuous patient observation with therapeutic communication and holistic assessment. This article reviews the major applications and opportunities of AI in mental health nursing, discusses ethical and professional challenges, and proposes future directions for education, research, clinical practice, and policy. AI should be regarded as a supportive technology rather than a replacement for professional nursing judgment or human therapeutic relationships.
The review found that AI tools—for example, selfreferral chatbots that reduced waiting times and increased treatment uptake—provide expanded 24/7 access, improved clinical efficiency, and potential for individualised personalisation, while Predictive models showed promise for treatment selection and risk stratification...
Shizal Nawaz, Laiba Nawaz, Hasnain Ali et al.· Digital Medicine· 0 citations
Artificial intelligence can enhance mental healthcare through early detection, personalized treatment planning, predictive risk assessment, digital therapeutics, and remote care delivery, and these applications may expand access to mental health services, particularly in underserved and resource-limited settings.
Fabiana Chyczij, M. Paixão, Diogo Gonçalves Sara· Global Health Economics and...· 0 citations
It is concluded that while AI integration into clinical psychology has the potential to substantially strengthen mental health systems, its successful implementation depends on adherence to ethical standards, fairness, and responsible governance.
O. Omankwu· International Journal of Eng...· 0 citations
Artificial intelligence is most defensible as a task-specific, clinically delimited support within stepped or blended care, with transparent role definition, version-specific validation, continuous safety monitoring, and accountable human oversight.
Mario Guadalupe López Ayala· Revista LINCE Ciencias Socia...· 0 citations
The role of AI in nursing practice, its impact on patient care and clinical decision-making, applications in nursing education, advantages, challenges, ethical considerations, and future implications for the nursing profession are discussed.
Sumit Padihar, Sunita Joshi, Ratna Parmar et al.· Genetics and Molecular Resea...· 0 citations
These technologies show promise in reducing human error and enhancing mental health care delivery; however, persistent challenges include data privacy, ethical considerations, and the need for diverse, large-scale datasets.
Juster Donal Sinaga· Journal of Society Counselin...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.