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.

Background: Artificial intelligence (AI) is a rapidly developing field with the potential to transform various aspects of health care and public health, including medical training. During the “Hygiene and Public Health” course for fifth-year medical students, a practical training session was conducted on vaccination using AI chatbots as an educational supportive tool. Before receiving specific training on vaccination, the students were given a web-based test extracted from the Italian National Medical Residency Test. After completing the test, a critical correction of each question was performed assisted by AI chatbots. Objective: The main aim of this study was to identify whether AI chatbots can be considered educational support tools for training in public health. The secondary objective was to assess the performance of different AI chatbots on complex multiple-choice medical questions in the Italian language. Methods: A test composed of 15 multiple-choice questions on vaccination was extracted from the Italian National Medical Residency Test using targeted keywords and administered to medical students via Google Forms and to different AI chatbot models (Bing Chat, ChatGPT, Chatsonic, Google Bard, and YouChat). The correction of the test was conducted in the classroom, focusing on the critical evaluation of the explanations provided by the chatbot. A Mann-Whitney U test was conducted to compare the performances of medical students and AI chatbots. Student feedback was collected anonymously at the end of the training experience. Results: In total, 36 medical students and 5 AI chatbot models completed the test. The students achieved an average score of 8.22 (SD 2.65) out of 15, while the AI chatbots scored an average of 12.22 (SD 2.77). The results indicated a statistically significant difference in performance between the 2 groups (U=49.5, P<.001), with a large effect size (r=0.69). When divided by question type (direct, scenario-based, and negative), significant differences were observed in direct (P<.001) and scenario-based (P<.001) questions, but not in negative questions (P=.48). The students reported a high level of satisfaction (7.9/10) with the educational experience, expressing a strong desire to repeat the experience (7.6/10). Conclusions: This study demonstrated the efficacy of AI chatbots in answering complex medical questions related to vaccination and providing valuable educational support. Their performance significantly surpassed that of medical students in direct and scenario-based questions. The responsible and critical use of AI chatbots can enhance medical education, making it an essential aspect to integrate into the educational system.

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).

Healthcare-associated infections (HAIs) pose significant challenges in healthcare systems, with preventable surveillance playing a crucial role. Traditional surveillance, although effective, is resource-intensive. The development of new technologies, such as artificial intelligence (AI), can support traditional surveillance in analysing an increasing amount of health data or meeting patient needs. We conducted a scoping review, following the PRISMA-ScR guideline, searching for studies of new digital technologies applied to the surveillance, control, and prevention of HAIs in hospitals and LTCFs published from 2018 to 4 November 2023. The literature search yielded 1292 articles. After title/abstract screening and full-text screening, 43 articles were included. The mean study duration was 43.7 months. Surgical site infections (SSIs) were the most-investigated HAI and machine learning was the most-applied technology. Three main themes emerged from the thematic analysis: patient empowerment, workload reduction and cost reduction, and improved sensitivity and personalization. Comparative analysis between new technologies and traditional methods showed different population types, with machine learning methods examining larger populations for AI algorithm training. While digital tools show promise in HAI surveillance, especially for SSIs, challenges persist in resource distribution and interdisciplinary integration in healthcare settings, highlighting the need for ongoing development and implementation strategies.

Background: 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.

Large Language Models (LLMs) have recently gathered attention with the release of ChatGPT, a user-centred chatbot released by OpenAI. In this Viewpoint, we retrace the evolution of LLMs to understand the revolution brought by ChatGPT. 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 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.