The Potential Impact of Large Language Models on Doctor-Patient Communication: A Case Study in Prostate Cancer.

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.

The Emerging Role of Large Language Models in Improving Prostate Cancer Literacy.

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.

Robotic-assisted vs. open ureteral reimplantation: a multicentre comparison.

PURPOSE: Open ureteral reimplantation is considered the standard surgical approach to treat distal ureteral strictures or injuries. These procedures are increasingly performed in a minimally invasive and robotic-assisted manner. Notably, no series comparing perioperative outcomes and safety of the open vs. robotic approach are available so far. METHODS: In this retrospective multi-center study, we compared data from 51 robotic ureteral reimplantations (RUR) with 79 open ureteral reimplantations (OUR). Both cohorts were comparatively assessed using different baseline characteristics and perioperative outcomes. Moreover, a multivariate logistic regression for independent predictors was performed. RESULTS: Surgery time, length of hospital stay and dwell time of bladder catheter were shorter in the robotic cohort, whereas estimated blood loss, postoperative blood transfusion rate and postoperative complications were lower than in the open cohort. In the multivariate linear regression analysis, robotic approach was an independent predictor for a shorter operation time (coefficient - 0.254, 95% confidence interval [CI] - 0.342 to - 0.166; p < 0.001), a lower estimated blood loss (coefficient - 0.390, 95% CI - 0.549 to - 0.231, p < 0.001) and a shorter length of hospital stay (coefficient - 0.455, 95% CI - 0.552 to - 0.358, p < 0.001). Moreover, robotic surgery was an independent predictor for a shorter dwell time of bladder catheter (coefficient - 0.210, 95% CI - 0.278 to - 0.142, p < 0.001). CONCLUSION: RUR represents a safe alternative to OUR, with a shorter operative time, decreased blood loss and length of hospital stay. Prospective research are needed to further define the extent of the advantages of the robotic approach over open surgery.

Monte Carlo Simulations of the Magnetic Behavior, Ordering Temperature and Magnetocaloric Effects in 1D, 2D and 3D Ferrimagnetic Systems.

As for the systematic investigations of magnetic behaviors and its related properties, computer simulations in extended quantum spin networks have been performed in good conditions via the generalized Ising model using the Monte Carlo-Metropolis algorithm with proven efficiencies. The present work, starting from a real magnetic system, provides detailed insights into the finite size effects and the ferrimagnetic properties in various 1 D, 2D and 3D geometries such as the magnetic moment, ordering temperature, and magnetocaloric effects with the different values of spins localized on the different coordinated sites.