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.