# The Role of AI-Based Chatbots in Public Health Emergencies: A Narrative Review
## 6 Clinical Insights from an Infectious Disease Specialist  
### AI-Powered Chatbots for Clinical Management During Outbreaks  
Infectious disease outbreaks pose significant challenges to global health security, placing immense strain on healthcare systems. The rapid spread of infection, coupled with often-limited resources, necessitates efficient and timely patient management. Artificial intelligence-powered tools, particularly chatbots, offer a promising avenue for enhancing patient care and optimizing resource allocation during these critical periods. Chatbots can provide readily accessible information, facilitate preliminary triage, and enable remote monitoring, contributing to a more sustainable global healthcare paradigm [90]. Artificial intelligence is rapidly transforming healthcare delivery within hospitals and clinics. Its applications span various areas, from enhancing clinical decision making and streamlining operational efficiency to improving diagnostic accuracy and revolutionizing patient care. The evolution of AI has led to the development of increasingly sophisticated chatbots with diverse healthcare applications [91]. Historically, programs like MYCIN [92] aided physicians with differential diagnosis based on patient symptoms. Modern chatbots can play a crucial role during infectious disease outbreaks by providing up-to-date information about symptoms, prevention measures, and treatment options. An example of such advanced AI technology is the BERT-based medical chatbot, which leverages Bidirectional Encoder Representations from Transformers (BERT) [93], a state-of-the-art deep learning model for natural language understanding (NLU). BERT, unlike traditional models that process text in a single direction (left-to-right or right-to-left), considers the context of a word by looking at both the words that come before and after it. This makes it highly effective in understanding the contextual meaning of sentences, especially in complex domains like medicine, where jargon and linguistic nuances are critical for accurate interpretation. Figure 3 illustrates the architecture of a BERT-based medical chatbot [94], where the BERT model processes user queries, extracts medical entities (such as symptoms or conditions), and generates relevant responses. Key components of the system include the user interface (frontend), which allows users to interact with the chatbot via text or voice input; the text processing module, which preprocesses queries by performing tokenization, stop word removal, stemming, lemmatization, and Part-of-Speech (PoS) tagging [95]; the BERT model, which parses text bidirectionally, recognizes medical entities and classifies user intent; context management, which maintains the history and context of the conversation to ensure consistent responses in complex interactions; the entity and intent recognition unit, which extracts medical entities and classifies intent using techniques such as Conditional Random Fields (CRFs) [96] and softmax [97]; dialogue management and response generation, which combines predefined templates, retrieval methods from a medical knowledge base, and generative templates to produce appropriate responses; the medical knowledge base, an up-to-date database with information on symptoms, conditions, treatments and guidelines, consulted by the chatbot to provide accurate advice; and finally, response delivery (backend), which sends the generated response to the user via the frontend interface, completing the interaction cycle. They can also facilitate appointment scheduling and answer frequently asked questions, reducing the burden on healthcare workers. In particular, AI's evolution in healthcare has been driven by advancements in machine learning and neural networks, coupled with increased computational power and data availability. These technologies are empowering healthcare professionals with tools to analyze patient data more effectively, leading to more precise diagnoses and personalized treatment plans.