AI CHATBOT FOR HEALTH EDUCATION BY NURSES: PATIENT ACCURACY AND ACCEPTANCE - Erik Kusuma; Dicky Edrian Kurniawan; Bagus Dwi Cahyono; Ainul Fitriyah (Journal of Vocational Nursing, 2026)
Introduction: AI-based chatbots are increasingly used in healthcare to deliver automated patient education due to their ability to provide instant responses and process large volumes of data. Despite their potential, concerns regarding accuracy, patient trust, and regulatory issues continue to limit their full integration into clinical practice. This systematic review evaluates the accuracy, acceptance, and effectiveness of chatbots in supporting nurse-led education. Methods: Adhering to PRISMA guidelines, a systematic search of five databases (2019–2024) identified 17 relevant studies. Data extraction focused on study design, chatbot interventions, evaluation metrics, and key findings. Results: The analysis revealed that chatbot accuracy ranged from 60% to 90%, with rule-based systems (92%) outperforming machine learning models (83%). Patient acceptance was moderate: 68% of users expressed willingness to use chatbots, but only 42% fully trusted their recommendations. Trust increased by 30% when chatbot responses were validated by healthcare professionals. Conclusions: AI chatbots can effectively address routine inquiries and reduce nurses’ workload; however, their limited emotional intelligence and contextual adaptability reduce their effectiveness compared to nurse-led education. Key challenges remain in terms of accuracy, trust, and regulation. Future research should focus on personalization, AI–human collaboration, and the development of ethical frameworks to ensure safe and effective implementation.