Natural language processing-based chatbots for chronic disease self-management: A systematic review of implementation and health outcomes

Background Conversational agents (chatbots) are increasingly used as digital health interventions to support chronic disease self-management. Advances in natural language processing (NLP) have improved their capacity for interactive dialogue and personalization, yet evidence regarding their implementation and clinical impact remains limited. Objectives This systematic review identifies and synthesizes studies implementing NLP-based chatbots for chronic disease self-management. Methods We searched seven electronic databases (PubMed, Embase, CINAHL, Web of Science, Scopus, Cochrane Library, and IEEE Xplore) and Google Scholar for studies published between January 2010 and November 2025. Studies evaluating NLP-based chatbots designed to support chronic disease self-management were deemed eligible. Study quality and risk of bias were assessed using the Mixed Methods Appraisal Tool and the Quality Assessment with Diverse Studies instrument. Results Six studies met the inclusion criteria; most were published in 2023 and targeted conditions such as cancer, diabetes, and hypertension. Chatbot functions primarily focused on symptom monitoring and disease-related education. Reported outcomes included improvements in disease-related knowledge, symptom burden, mental well-being, and self-care adherence. Usability and acceptability were generally favorable, with high satisfaction, perceived usefulness, and engagement. However, evidence of objective clinical benefits, including laboratory outcomes, was limited. Technical architectures varied widely, and advanced NLP capabilities—such as free-text natural language understanding—were rarely implemented. Conclusions NLP-based chatbots show promise for supporting chronic disease self-management, particularly for psychosocial and behavioral outcomes. However, evidence of clinical efficacy remains limited. Future research should prioritize adaptive, context-aware designs and standardized outcome frameworks aligned with real-world self-management needs.