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.
Artificial intelligence (AI) is transforming mental health care by enabling novel approaches to assessment, outcome prediction, and treatment. However, the rapid growth of AI tools has outpaced evidence synthesis on their realworld clinical value, implementation barriers, and ethical risks, leaving clinicians and policymakers without clear guidance for responsible integration. This narrative review aimed to examine current AI applications in mental health care, identify their clinical benefits, implementation barriers, and ethical challenges, and define conditions for responsible integration that preserve the patient-clinician relationship. A structured literature search of PubMed, PsycINFO, and Scopus was conducted for peer reviewed English language articles published between January 2019 and March 2026. Studies addressing AI applications, benefits, implementation, or ethics in mental health were included and synthesised narratively into thematic categories. 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. Predictive models showed promise for treatment selection and risk stratification, while natural language processing unlocked unstructured clinical data. However, concerns included patient data privacy, algorithmic bias that may worsen existing inequities, potential erosion of the therapeutic relationship, and mixed acceptance among clinicians and patients. Many AI applications remain experimental, and regulatory frameworks have not kept pace with technological developments. This review did not include formal quality appraisal or quantitative synthesis; the evidence is limited by short followup periods, high dropout in chatbot trials, and a predominance of studies from highincome countries. Future research should employ longitudinal, codesigned, mixedmethods designs and pragmatic trials that evaluate clinical outcomes alongside equity, user trust, and the preservation of empathic, humanled care—rather than relying solely on uncontrolled implementation studies. AI should be responsibly integrated to augment, not replace, the clinical workforce. Successful application requires a balance between technological advancement, patient protection, and preservation of the patient-clinician relationship.
Shizal Nawaz, Laiba Nawaz, Hasnain Ali et al.· Digital Medicine· 0 citations
Mental disorders are among the leading causes of disability worldwide, imposing burdens on individuals, healthcare systems, and economies. Shortages of mental health professionals, unequal access to care, and increasing demand highlight the need for innovative solutions. Artificial intelligence (AI) has emerged as a technology with potential to transform mental healthcare through improved diagnosis, treatment, monitoring, and service delivery. This review examines AI applications in global mental health, focusing on opportunities, challenges, and implications for health equity. A narrative review was conducted using PubMed, Scopus, and Web of Science. Studies addressing AI technologies, digital mental health interventions, predictive analytics, telepsychiatry, health system integration, and mental health were identified and synthesized. Findings indicate that AI can enhance mental healthcare through early detection, personalized treatment planning, predictive risk assessment, digital therapeutics, and remote care delivery. These applications may expand access to mental health services, particularly in underserved and resource-limited settings, while supporting sustainability through resource allocation and data-driven decision-making. However, challenges include data privacy, algorithmic bias, limited transparency, regulatory uncertainty, and the risk of exacerbating health inequalities. Effective implementation requires governance, culturally appropriate applications, and preservation of human-centered care. Future developments in personalized psychiatry, explainable AI, and integration into public health systems may expand AI’s role in mental healthcare. Overall, AI represents a tool for addressing global mental health challenges, but its benefits depend on responsible, equitable, and ethical implementation. Collaboration among healthcare professionals, researchers, policymakers, and technology developers is essential to ensure AI improves outcomes and supports sustainable healthcare systems worldwide.
Fabiana Chyczij, M. Paixão, Diogo Gonçalves Sara· Global Health Economics and...· 0 citations
The integration of artificial intelligence (AI) and psychology presents significant opportunities to
enhance mental health diagnosis, intervention, and clinical decision-making. This study
examines how AI-driven technologies, including machine learning, natural language processing,
and digital phenotyping, are reshaping psychological research and mental health care delivery. A
mixed-methods approach was employed, combining a focused literature review with a survey of
mental health professionals and individuals with lived experience. The findings highlight AI’s
growing role in early symptom detection, personalised therapeutic interventions, and predictive
diagnostics. However, persistent ethical challenges, particularly those related to algorithmic
bias, data privacy, and transparency, remain significant barriers to widespread adoption. The
study concludes 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 increasingly used in mental health screening, symptom monitoring, conversational support, and low-intensity digital interventions. This structured critical narrative review examines the clinical promise and ethical boundaries of artificial intelligence in anxiety care. A targeted search of PubMed/MEDLINE, official publisher platforms, institutional guidance, and reference lists was updated through June25,2026. Sources were assessed across seven analytical domains: technological class
and intended function, anxiety-related applicability, strength of effectiveness and safety evidence, user experience and persuasive design, ethical and professional risk, equity, and human oversight. Evidence indicates that selected conversational interventions can produce small-to-moderate short-term reductions in anxiety symptoms, but findings are heterogeneous and cannot be generalized from bounded, clinically curated systems to unrestricted consumer chatbots. Persistent risks include delayed escalation, automation of empathy, reassurance dependence, privacy concerns, subgroup bias, and unequal digital access. Artificial intelligence is therefore 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
Artificial Intelligence (AI) is rapidly transforming healthcare by supporting clinical decision-making, improving patient safety, streamlining documentation, and strengthening nursing education. Nursing professionals are increasingly using AI-enabled technologies such as clinical decision-support systems, predictive analytics, virtual assistants, remote patient monitoring, natural language processing, robotics, and intelligent electronic health records. These technologies can assist nurses in identifying patient deterioration, prioritizing care, reducing medication errors, managing large volumes of clinical information, and developing individualized care plans. In nursing education, AI provides opportunities for adaptive learning, virtual simulation, automated assessment, personalized feedback, and development of clinical reasoning skills. However, successful implementation requires attention to ethical issues, data privacy, algorithmic bias, cybersecurity, professional accountability, technological literacy, and the preservation of human-centered care. Nurses must therefore develop appropriate AI literacy while maintaining critical thinking and clinical judgment. This article discusses 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.
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
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