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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]