# The Role of AI-Based Chatbots in Public Health Emergencies: A Narrative Review
## 5 Chatbots as Statistical Consultants in Infectious Disease Settings: Potential and Reliability
### Evaluating the Reliability of Chatbots in Recommending Statistical Techniques  
Although artificial intelligence chatbots such as ChatGPT have considerable potential to offer statistical guidance, their reliability in providing accurate and contextually appropriate recommendations is a critical issue. Language models are trained on vast datasets, often encompassing a wide range of disciplines, including statistics. However, they lack a deep understanding of the specific nuances of a given dataset or the subtleties of research questions in infectious disease contexts. This raises several reliability issues:  
1. Accuracy of recommendations: AI chatbots are not immune from making mistakes or offering incorrect statistical advice. Although ChatGPT may suggest well-known statistical methods, it does not always recommend the techniques best suited to a specific dataset. For example, it might suggest the use of a linear regression when, given the distribution of the data, a nonlinear model would be more appropriate. Studies have shown that artificial intelligence models, despite their sophistication, are often unable to fully understand the domain-specific requirements of medical or epidemiological datasets [85]. This limitation could lead to erroneous recommendations, especially when users are unfamiliar with the subtleties of statistical techniques. 2. Assumptions and limitations: Every statistical technique has inherent assumptions, such as the normality of data distribution, independence of observations, or homoscedasticity. Chatbots do not always provide sufficient warning or detail about these assumptions, potentially leading to the misuse of statistical methods. For example, recommending ANOVA (analysis of variance) without clarifying the need for homogeneity of variances could lead to erroneous analysis results [86]. In this regard, human statisticians possess the nuances of judgment necessary to adapt methods based on data anomalies, something that current chatbots lack. 3. Interpretability and user understanding: Even when ChatGPT correctly identifies an appropriate statistical method, explaining why a particular technique should be used remains a challenge. Although the model can describe statistical concepts in general terms, it does not always provide clear guidance on why certain assumptions are important or how to interpret the results in a meaningful way. This limitation could be particularly problematic for users who do not have a strong background in statistics. A chatbot might recommend logistic regression for analyzing binary outcomes, but not guide the user through the necessary diagnostic tests, such as testing for multicollinearity or overfitting [87]. The absence of in-depth explanations could hinder the user's ability to effectively apply the recommended techniques. 4 Dependence on training data: ChatGPT and similar models are trained on large datasets that include vast amounts of information, but are not necessarily up-to-date with the latest advances in statistical theory or infectious disease epidemiology. This lag in knowledge could cause the chatbot to suggest outdated or less efficient methods. In addition, these models are based on probabilities derived from training data, which means they lack a true understanding of the problem at hand. They generate answers based on patterns and correlations in the data they have seen, but they do not reason about the statistical principles involved as a human expert would [88]. 5. Risk of misinformation: One of the significant risks of using AI chatbots for statistical guidance is the potential for misinformation. ChatGPT, while very adept at generating human-like text, can sometimes produce \"hallucinations\", generating plausible but factually incorrect information [89]. In a statistical context, this could lead the chatbot to recommend inappropriate or even nonexistent methods. For example, it might suggest a test or statistical procedure that is not applicable to the user's dataset or, in extreme cases, does not exist in the statistical literature.