The objective of this study was to explore the feature of generative artificial intelligence (AI) in asking sexual health among cancer survivors, which are often challenging for patients to discuss. We employed the Generative Pre-trained Transformer-3.5 (GPT) as the generative AI platform and used DocsBot for citation retrieval (June 2023). A structured prompt was devised to generate 100 questions from the AI, based on epidemiological survey data regarding sexual difficulties among cancer survivors. These questions were submitted to Bot1 (standard GPT) and Bot2 (sourced from two clinical guidelines). No censorship of sexual expressions or medical terms occurred. Despite the lack of reflection on guideline recommendations, 'consultation' was significantly more prevalent in both bots' responses compared with pharmacological interventions, with ORs of 47.3 (p<0.001) in Bot1 and 97.2 (p<0.001) in Bot2. Generative AI can serve to provide health information on sensitive topics such as sexual health, despite the potential for policy-restricted content. Responses were biased towards non-pharmacological interventions, which is probably due to a GPT model designed with the 's prohibition policy on replying to medical topics. This shift warrants attention as it could potentially trigger patients' expectations for non-pharmacological interventions.
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http://dx.doi.org/10.1136/bmjhci-2023-100924 | DOI Listing |
J Med Internet Res
December 2024
Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Background: Large language models (LLMs) are increasingly integrated into medical education, with transformative potential for learning and assessment. However, their performance across diverse medical exams globally has remained underexplored.
Objective: This study aims to introduce MedExamLLM, a comprehensive platform designed to systematically evaluate the performance of LLMs on medical exams worldwide.
J Comput Biol
December 2024
Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, Canada.
Image-to-image translation has gained popularity in the medical field to transform images from one domain to another. Medical image synthesis via domain transformation is advantageous in its ability to augment an image dataset where images for a given class are limited. From the learning perspective, this process contributes to the data-oriented robustness of the model by inherently broadening the model's exposure to more diverse visual data and enabling it to learn more generalized features.
View Article and Find Full Text PDFWorld J Urol
December 2024
Department of Urology, Baldwin Park Medical Center, Kaiser Permanente, 1011 Baldwin Park Blvd., Baldwin Park, CA, 91706, USA.
Purpose: To evaluate the accuracy, comprehensiveness, empathetic tone, and patient preference for AI and urologist responses to patient messages concerning common BPH questions across phases of care.
Methods: Cross-sectional study evaluating responses to 20 BPH-related questions generated by 2 AI chatbots and 4 urologists in a simulated clinical messaging environment without direct patient interaction. Accuracy, completeness, and empathetic tone of responses assessed by experts using Likert scales, and preferences and perceptions of authorship (chatbot vs.
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