AI-Driven Chatbots in Public Health: A Review of Applications, Evidence, and Challenges

Chatbots driven by artificial intelligence (AI) are increasingly used to deliver health information, screening, counselling, and behavioural support at population scale. This paper reviews five recent secondary studies, namely an umbrella review of 44 systematic reviews, a systematic review of large language model (LLM) chatbots, and three scoping reviews of COVID-19 and public health chatbots, to consolidate what is known about how these tools are designed, where they are applied, and how well they work. Most deployed chatbots are text-based, rely on simple decision-tree or hybrid architectures, and are concentrated in pandemic response, mental health, and health-information provision. Evidence of effectiveness is encouraging for smoking cessation and for short-term reductions in depression and anxiety, but mixed for lifestyle change and clinical decision support, and benefits often fade at follow-up. Persistent weaknesses include a shortage of randomised trials, limited use of theory and user-centred design, inconsistent reporting, readability and accuracy problems in LLM output, slow safety escalation, privacy risks, and digital-divide inequities. The paper closes with priorities for standardised evaluation, domain-specific model development, safeguarding, and inclusive design.