# The Role of AI-Based Chatbots in Public Health Emergencies: A Narrative Review
## 1 Introduction
### From Early Chatbots to Generative Pre-Trained Transformers  
After ALICE (Artificial Linguistic Internet Computer Entity) [33], the real breakthrough for chatbots began with the advent of generative pre-trained transformers (GPTs) in the mid-2010s [6]. GPTs enabled chatbots to generate more human-like text and understand context with greater accuracy. ELMo (Embedding from Language Model), BERT (Bidirectional Encoder Representations from Transformers) and the more recent Large Language Models (LLMs), like GPT-4, all represent advanced architectures of artificial neural networks, trained on vast amounts of human texts and able to produce human-like responses [34]. In public health, modern chatbots are now being deployed to assist in disease surveillance, health education, and behavior change interventions [35], as discussed in the remainder of this review. From a psychological perspective, LLM-based chatbots can be powerful for public health communication since they (i) are capable of personification [18], like PERRY, fostering engagement; (ii) can be subject to the ELIZA effect [35], fostering trust; and (iii) can display basic elements of a theory of mind [36]. However, LLMs are also complex systems, trained via reinforcement learning to closely reproduce human data [35]. Consequently, LLM-based chatbots also end up inheriting human biases [37]. In cognitive psychology, biases are deformations from common ways of combining intuitive and logical reasoning when processing knowledge [18]. Biases can be considered cognitive shortcuts reducing the effort required for processing information. For instance, confirmation bias makes it easier to accommodate novel knowledge if it confirms one's own pre-existing information. In public health communication, LLMs displaying biases similar to human communicators' might be considered neutral addition to the system. However, LLMs also possess non-human biases, due to their artificial nature [37]: (i) myopic overconfidence, e.g., pushing through inaccurate reasoning while appearing overly self-confident, and (ii) hallucinations, i.e., attempting to satisfy users' requests while interpolating over missing data from training knowledge. In public health contexts, hallucinations represents a deeply concerning phenomenon. Hallucinations might range from subtle--like fake but vaguely familiar scientific papers mentioned as sources backing up health facts [38]--to outright disastrous--like recommending toxic levels of herbal remedies for clinical pathologies [39]. Hallucinations, like myopic overconfidence, stem from two key factors. On the one hand, the reinforcement learning used to train GPTs pushes LLMs to always provide an answer, even in the presence of faulty, missing, or inaccurate data [37]. On the other hand, LLMs and the chatbots based on them do not possess metacognitive skills for filtering out less reliable information. This is different from what humans can do, e.g., filtering out dubious info about vaccines coming from extremely unreliable sources [40]. This lack of metacognitive skills makes LLMs poor fact searchers and providers. Because of myopic overconfidence, LLM-based chatbots can produce recommendations sounding fair but ultimately completely false. For instance, [PERSON] and colleagues [38] found that, while producing 50 small literature reviews on [PERSON] disease, GPT 3.5 produced references with major hallucinations in 86.7% of the cases. Always when investigating GPT 3.5, recent studies [39; 41] found that the LLM recommended to patients with the metabolic dysfunction-associated statotic liver disease using herbal remedies that are considered by experts as harmful. These concrete examples underline the grave consequences that hallucinations can have over LLMs' performance as public health chatbots.