Artificial intelligence across community, mental and child health nursing: a future-ready framework for precision, preventive and person centered care
Artificial intelligence (AI) is transforming healthcare by enabling precision, predictive, and person-centered approaches that enhance the quality, accessibility, and efficiency of nursing care. Community, mental, and child health nursing are increasingly benefiting from AI-driven innovations, including machine learning, deep learning, natural language processing, predictive analytics, clinical decision support systems, wearable technologies, conversational AI, and telehealth platforms. These technologies facilitate early disease detection, population health surveillance, risk prediction, personalized interventions, remote patient monitoring, developmental screening, mental health assessment, and evidence-based clinical decision-making. This narrative review synthesizes contemporary evidence on the integration of AI across community, mental, and child health nursing and explores its implications for preventive healthcare, health promotion, and holistic patient management. Relevant literature published between 2018 and 2026 was identified through major electronic databases, including PubMed, Scopus, Web of Science, CINAHL, Embase, PsycINFO, IEEE Xplore, and Google Scholar. The reviewed evidence demonstrates that AI improves healthcare accessibility, strengthens continuity of care, enhances diagnostic accuracy, supports personalized nursing interventions, and optimizes resource utilization. However, successful implementation requires addressing challenges related to ethical governance, algorithmic bias, data privacy, cybersecurity, interoperability, digital infrastructure, and AI competency among nurses. The review concludes that AI should function as a clinical decision-support tool that complements, rather than replaces, professional nursing judgment. Strategic investment in digital health education, interdisciplinary collaboration, and responsible AI governance will be essential for developing future-ready nurses capable of delivering equitable, evidence-based, and compassionate healthcare across diverse populations.
Authors
- Sukabhogi Anusha
- Akanksha Anjali (ORCID: https://orcid.org/0009-0002-3755-8641)
- Virat Shankar Gore
- Gomathi Munusamy (ORCID: https://orcid.org/0000-0002-3775-9358)
- Mohammed Umar (ORCID: https://orcid.org/0000-0001-8249-3803)
- Kumar Pradeep
- Trilok Chand Sharma
- E. Balamurugan
- Ghanshyam Singh
- Gomathi
- Joy Priscilla
- K. Rakhee Sarkar
Institutions
- Maharashtra University of Health Sciences (IN)
- Tamil Nadu Dr. M.G.R. Medical University (IN)
- King George's Medical University (IN)
- Rajiv Gandhi University of Health Sciences (IN)
- Uttar Pradesh University of Medical Sciences (IN)
Publication Details
- Journal
- International Journal of Community Medicine and Public Health
- Published
- 2026-10-30
- DOI
- https://doi.org/10.18203/2394-6040.ijcmph20264136
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00