INTEGRATING ARTIFICIAL INTELLIGENCE, DIGITAL HEALTH, AND GENOMIC MEDICINE INTO EVIDENCE-BASED NURSING PRACTICE: ADVANCING MENTAL HEALTH, AGED CARE, PUBLIC HEALTH, AND PATIENT SAFETY

Abstract

AI, digital health and genomic medicine are increasingly transforming evidence-based nursing by augmenting the potential for prediction, ongoing monitoring, personalized intervention and precision-based care. This review examines how these technologies are being integrated into nursing practice across mental health, aged care, public health, and patient safety. Digital health improves nursing practice with telehealth and mobile applications, wearable monitoring, connected care, and digital therapeutics, while AI enhances risk prediction, clinical decision-making, NLP, triage, and care planning. The contribution from genomic medicine includes: Hereditary Risk Assessment, Pharmacogenomics, Biomarker-Informed Care, and Precision Nursing. These technologies can enhance the early detection of patient health conditions, access to health services, surveillance, and care personalization, medication safety, and continuity of care across priority clinical domains. But data quality, interoperability and algorithmic bias are limiting factors, as well as concerns around privacy, transparency, workforce readiness and professional accountability. What is needed next is clinically proven, interoperable, and equitable systems that support, but don't supplant, nursing judgement, and are explainable and human-centred. Clinically robust, ethically sound governance mechanisms and person-centred care combined with technological innovation will play a vital role in the development of safe and evidence-informed precision nursing.