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Background: In recent years, the integration of large language models (LLMs) into healthcare has emerged as a revolutionary approach to enhancing doctor–patient communication, particularly in the management of diseases such as prostate cancer. Methods: Our paper evaluated the effectiveness of three prominent LLMs—ChatGPT (3.5), Gemini (Pro), and Co-Pilot (the free version)—against the official Romanian Patient's Guide on prostate cancer. Employing a randomized and blinded method, our study engaged eight medical professionals to assess the responses of these models based on accuracy, timeliness, comprehensiveness, and user-friendliness. Results: The primary objective was to explore whether LLMs, when operating in Romanian, offer comparable or superior performance to the Patient's Guide, considering their potential to personalize communication and enhance the informational accessibility for patients. Results indicated that LLMs, particularly ChatGPT, generally provided more accurate and user-friendly information compared to the Guide. Conclusions: The findings suggest a significant potential for LLMs to enhance healthcare communication by providing accurate and accessible information. However, variability in performance across different models underscores the need for tailored implementation strategies. We highlight the importance of integrating LLMs with a nuanced understanding of their capabilities and limitations to optimize their use in clinical settings. [ABSTRACT FROM AUTHOR]

This study assesses the effectiveness of chatbots powered by Large Language Models (LLMs)—ChatGPT 3.5, CoPilot, and Gemini—in delivering prostate cancer information, compared to the official Patient's Guide. Using 25 expert-validated questions, we conducted a comparative analysis to evaluate accuracy, timeliness, completeness, and understandability through a Likert scale. Statistical analyses were used to quantify the performance of each model. Results indicate that ChatGPT 3.5 consistently outperformed the other models, establishing itself as a robust and reliable source of information. CoPilot also performed effectively, albeit slightly less so than ChatGPT 3.5. Despite the strengths of the Patient's Guide, the advanced capabilities of LLMs like ChatGPT significantly enhance educational tools in healthcare. The findings underscore the need for ongoing innovation and improvement in AI applications within health sectors, especially considering the ethical implications underscored by the forthcoming EU AI Act. Future research should focus on investigating potential biases in AI-generated responses and their impact on patient outcomes. [ABSTRACT FROM AUTHOR]

Background : Cancer is a major public health problem. Four million new cancer cases are diagnosed annually in Europe, of which around 40% could be prevented. The European Code Against Cancer (ECAC) is a health education tool to raise awareness about risk factors and evidence-based measures to prevent cancer. The ECAC 4th edition consists of 12 recommendations to reduce individuals' cancer risk and related deaths. This study explores perceived barriers to adopting the cancer prevention actions recommended by the ECAC fourth edition in the European Union (EU). Methods : The COM-B model of behaviour change will be used as a framework for the design and analysis of the study since it identifies factors (capability, opportunity, motivation) that need to be present for any behaviour change to occur. A qualitative study using an exploratory research methodology was designed to obtain information from adults with no previous cancer diagnosis by employing in-depth semi-structured interviews. Participants are selected using a quota sampling strategy according to sex, age, and education level (18 profiles/country). Interviews will be conducted in participants' native language by trained researchers. Afterward, a thematic content analysis will be conducted to identify common topics, followed by a critical analysis of their discourse. Discussion : This study will contribute to informing the fifth edition of the ECAC –currently under development; by providing insights on how EU citizens perceive the cancer prevention recommendations of the fourth edition and whether they can motivate them to change unhealthy behaviours. Additionally, the study will help to identify the main facilitators and barriers (perceived or already confronted) to adopt these cancer prevention recommendations so that this knowledge will contribute to updating the recommendations of the fifth edition of the ECAC. [ABSTRACT FROM AUTHOR]

Many countries worldwide had difficulties reaching a sufficiently high vaccination uptake during the COVID-19 pandemic. Given this context, we collected data from a panel of 30,000 individuals, which were representative of the population of Romania (a country in Eastern Europe with a low 42.6% vaccination rate) to determine whether people are more likely to be connected to peers displaying similar opinions about COVID-19 vaccination. We extracted 443 personal networks, amounting to 4430 alters. We estimated multilevel logistic regression models with random-ego-level intercepts to predict individual opinions about COVID-19 vaccination. Our evidence indicates positive opinions about the COVID-19 vaccination cluster. Namely, the likelihood of having a positive opinion about COVID-19 vaccination increases when peers have, on average, a more positive attitude than the rest of the nodes in the network (OR 1.31, p < 0.001). We also found that individuals with higher education and age are more likely to hold a positive opinion about COVID-19 vaccination. With the given empirical data, our study cannot reveal whether this assortative mixing of opinions is due to social influence or social selection. However, it may nevertheless have implications for public health interventions, especially in countries that strive to reach higher uptake rates. Understanding opinions about vaccination can act as an early warning system for potential outbreaks, inform predictions about vaccination uptake, or help supply chain management for vaccine distribution. [ABSTRACT FROM AUTHOR]

Background: Most studies assessing the impact of online media and social media use on COVID-19 vaccine hesitancy predominantly rely on survey data, which often fail to capture the clustering of health opinions and behaviors within real-world networks. In contrast, research using social network analysis aims to uncover the diverse communities and discourse themes related to vaccine support and hesitancy within social media platforms. Despite these advancements, there is a gap in the literature on how a person's social circle affects vaccine acceptance, wherein an important part of social influence stems from offline interactions., Objective: We aimed to examine how online media consumption influences vaccination decisions within real-world social networks by analyzing unique quantitative network data collected from Romania, an Eastern European state and member of the European Union., Methods: We conducted 83 face-to-face interviews with participants from a living lab in Lerești, a small rural community in Romania, using a personal network analysis framework. This approach involved gathering data on both the respondents and individuals within their social circles (referred to as alters). After excluding cases with missing data, our analysis proceeded with 73% (61/83) of the complete personal networks. To examine the hierarchical structure of alters nested within ego networks, we used a mixed multilevel logistic regression model with random intercepts. The model aimed to predict vaccination status among alters, with the focal independent variable being the respondents' preferred source of health and prevention information. This variable was categorized into 3 types: traditional media, online media (including social media), and a combination of both, with traditional media as the reference category., Results: In this study, we analyzed 61 personal networks, encompassing between 15 and 25 alters each, totaling 1280 alters with valid data across all variables of interest. Our primary findings indicate that alters within personal networks, whose respondents rely solely on online media for health information, exhibit lower vaccination rates (odds ratio [OR] 0.37, 95% CI 0.15-0.92; P=.03). Conversely, the transition from exclusive traditional media use to a combination of both traditional and online media does not significantly impact vaccination rate odds (OR 0.75, 95% CI 0.32-1.78; P=.52). In addition, our analysis revealed that alters in personal networks of respondents who received the vaccine are more likely to have received the vaccine themselves (OR 3.75, 95% CI 1.79-7.85; P<.001)., Conclusions: Real-world networks combine diverse human interactions and attributes along with consequences on health opinions and behaviors. As individuals' vaccination status is influenced by how their social alters use online media and vaccination behavior, further insights are needed to create tailored communication campaigns and interventions regarding vaccination in areas with low levels of digital health literacy and vaccination rates, as Romania exposes., (©Iulian Oană, Marian-Gabriel Hâncean, Matjaž Perc, Jürgen Lerner, Bianca-Elena Mihăilă, Marius Geantă, José Luis Molina, Isabela Tincă, Carolina Espina. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 25.10.2024.)