Public health is a multidisciplinary field that aims to promote and protect the health of communities through various interventions, such as disease prevention, health promotion, and policy development. It involves analyzing data and applying evidence-based approaches to improve the health outcomes of populations (1–3). One of the main challenges in public health is the emergence and re-emergence of infectious diseases such as COVID-19, which can pose a significant threat to public health (4). Other challenges include the rise of non-communicable diseases (NCDs) such as heart disease and cancer, which are often linked to lifestyle factors such as poor diet and physical inactivity (5). Health inequities also pose a challenge to public health, as some groups, such as marginalized and vulnerable populations, may experience poorer health outcomes due to commercial determinants of health (1, 3, 5). Furthermore, there are challenges associated with the collection, management, and analysis of public health data. Ensuring data privacy and security, addressing biases in data collection and analysis, and making data accessible to all stakeholders are all critical issues in the field of public health. Finally, the need for effective communication and collaboration among stakeholders is essential to address these challenges and improve public health outcomes (2, 6, 7).

The growth of Artificial Intelligence (AI) in healthcare has been exponential in recent years, with advancements in machine learning, natural language processing (NLP), and image analysis. AI is increasingly being used to improve disease surveillance, drug discovery, and personalized medicine, among other applications, with the potential to transform healthcare delivery (8–10). Also, AI methods have shown great promise in addressing various public health challenges. Machine learning algorithms, NLP, and other AI techniques can be used to analyze large datasets, identify patterns and trends, and generate insights that can inform public health interventions (11, 12). ChatGPT is a state-of-the-art NLP model developed by OpenAI that has shown impressive performance in a variety of tasks, including language translation, text completion, and sentiment analysis. The model's ability to generate coherent and contextually appropriate responses to text inputs has made it a promising tool for a wide range of applications, including public health (13). For example, ChatGPT can be used to help patients manage chronic conditions by providing reminders for medication, diet, and exercise, and answering questions about symptoms and treatment options. ChatGPT can also be used to help patients find healthcare providers, schedule appointments, and access healthcare information. Moreover, ChatGPT can be used to improve patient engagement and education. Patients can interact with ChatGPT in natural language and receive tailored responses based on their medical history, preferences, and needs. ChatGPT can also provide patients with reliable and up-to-date health information, such as disease prevention tips, symptom management advice, and resources for mental health support (13–15).

In this paper, we conducted a SWOT analysis and PESTLE analysis (16, 17) to evaluate the applying of ChatGPT in public health. SWOT stands for Strengths, Weaknesses, Opportunities, and Threats, and is a widely used strategic planning tool that helps in identifying the internal and external factors that can impact the success of a project or initiative (17). As well as PESTLE analysis is a strategic planning tool used to assess and analyze external factors that can impact an organization, project, or industry. It examines the Political, Economic, Sociocultural, Technological, Legal, and Environmental factors that can influence the environment in which an entity operates (16). By conducting a SWOT analysis of ChatGPT, we aim to identify the strengths, weaknesses, opportunities, and threats associated with the application of this technology in public health. Also, we want to determine main external factors that can have an effect on applying this technology in public health.

The methods used in this paper involve a comprehensive literature review of previous studies and publications related to the applications of ChatGPT in public health (18). To identify pertinent studies for our research, a comprehensive search was conducted in reputable databases, employing relevant keywords. The databases used for this purpose were Pubmed, Scopus, and Google Scholar. The selection of relevant articles was based on a combination of crucial keywords that proved effective in narrowing down the search results. The selected keywords for this study were “ChatGPT” AND (“public health” OR “healthcare”). To ensure consistency and focus, the search was limited to English-language papers published before April 15, 2023. Through this systematic approach, a total of 106 articles were initially identified across the three aforementioned databases. After evaluation of these articles based on their alignment with the research topic, 16 papers were ultimately deemed relevant and included for further analysis. After identifying relevant articles, a qualitative content analysis was conducted to extract and classify relevant information related to the SWOT analysis and PESTLE analysis of ChatGPT in public health.

The extracted data were organized into four categories: strengths, weaknesses, opportunities, and threats.

The application of ChatGPT in public health can be analyzed using the PESTLE framework, which examines the Political, Economic, Sociocultural, Technological, Legal, and Environmental factors that influence its implementation and impact. This analysis aims to provide a comprehensive understanding of the external factors that shape the context of ChatGPT's application in public health.

The findings highlight the potential benefits and limitations of using chatbots powered by ChatGPT in the public health context. Consequently, they can provide personalized health information and support to individuals, disease surveillance, and outbreak detection, besides facilitating individual and shared decision-making. Nevertheless, there are also limitations associated with ChatGPT that should be considered, such as the potential misinterpretation or miscommunication, privacy concerns, and the risk of perpetuating biases in health data. By conducting a PESTLE analysis, policymakers, healthcare organizations, and researchers can gain insights into the broader contextual factors that can influence the application of ChatGPT in public health. This analysis can inform decision-making, help anticipate challenges, and guide the development of ethical guidelines and regulatory frameworks to maximize the benefits and mitigate potential risks associated with ChatGPT's implementation in the public health domain.

Indeed, future studies must clarify whether the advantages of utilizing chatbots outweigh their risks in assisting public health actions. Although artificial intelligence-based approaches can improve healthcare outcomes, the threats should be carefully considered to avoid inappropriate decision-making and the deepening of health inequalities especially the digital divide which continues to grow especially in the Global South. Hence, the widespread adoption of the disruptive technology of AI chatbots in public health will require careful oversight and time as authorities must first understand the optimal scenarios for the ethics and legalities of its implementation and application. However, these actions cannot be delayed because hundreds of AI tools are being released and are being used for learning about public health whether by design or not.

PPM and SA: conceptualization and writing original draft. JK, SA, ML, PM, and AO: writing review and editing. PPM, SA, JK, ML, PM, and AO: conceptualization, supervision, and writing review and editing. All authors contributed to the article and approved the submitted version.