Large Language Models (LLMs) have recently gathered attention with the release of ChatGPT, a user-centered chatbot released by OpenAI. In this perspective article, we retrace the evolution of LLMs to understand the revolution brought by ChatGPT in the artificial intelligence (AI) field.The opportunities offered by LLMs in supporting scientific research are multiple and various models have already been tested in Natural Language Processing (NLP) tasks in this domain.The impact of ChatGPT has been huge for the general public and the research community, with many authors using the chatbot to write part of their articles and some papers even listing ChatGPT as an author. Alarming ethical and practical challenges emerge from the use of LLMs, particularly in the medical field for the potential impact on public health. Infodemic is a trending topic in public health and the ability of LLMs to rapidly produce vast amounts of text could leverage misinformation spread at an unprecedented scale, this could create an “AI-driven infodemic,” a novel public health threat. Policies to contrast this phenomenon need to be rapidly elaborated, the inability to accurately detect artificial-intelligence-produced text is an unresolved issue.

We describe the epidemiology of New Delhi Metallo-Beta-Lactamase-Producing Carbapenem-Resistant Enterobacterales (NDM-CRE) colonization/infection in a cohort of COVID-19 patients in an Italian teaching hospital. These patients had an increased risk of NDM-CRE acquisition versus the usual patients (75.9 vs. 25.3 cases/10,000 patient days). The co-infection significantly increased the duration of hospital stay (32.9 vs. 15.8 days).

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Vaccination of healthcare workers (HCWs) is a crucial element to overcome the COVID-19 pandemic. The aim of this survey was to assess attitudes, sources of information and practices among Italian Healthcare workers (HCWs) in relation to COVID-19 vaccination. Methods: From 19 February to 23 April 2021, an anonymous voluntary questionnaire was sent to the mailing list of the main National Health Service structures. Data were collected through the SurveyMonkey platform. Results: A total of 2137 HCWs answered. Hesitancy towards COVID-19 vaccination was more frequent in females, in those with lower concern about COVID-19, and in nurses, auxiliary nurses (AN) and healthcare assistants. Hesitant professionals were more likely to not recommend vaccination to their patients or relatives, while a high concern about COVID-19 was related to an increased rate of recommendation to family members. HCWs were mostly in favor of mandatory vaccination (61.22%). Female sex, a lower education level, greater hesitancy and refusal to adhere to flu vaccination campaigns were predictors influencing the aversion to mandatory vaccination. All categories of HCWs referred mainly to institutional sources of information, while scientific literature was more used by professionals working in the northern regions of Italy and in infection control, infectious diseases, emergencies and critical areas. HCWs working in south-central regions, nurses, AN, healthcare technicians, administrators and HCWs with a lower education level were more likely to rely on internet, television, newspapers, and the opinions of family and friends. Conclusions: Communication in support of COVID-19 immunization campaigns should consider the differences between the various HCWs professional categories in order to efficiently reach all professionals, including the most hesitant ones.