Abosamak Nour, Namoos Asmaa, Golob Deeb Janina, Gal Tamas
Virginia Commonwealth University, Richmond, Virginia.
AMIA Jt Summits Transl Sci Proc. 2025 Jun 10;2025:42-45. eCollection 2025.
Oral and oropharyngeal cancers disproportionately affect Black Americans, contributing to significant healthcare disparities due to late-stage diagnoses and limited awareness. AI-powered chatbots have the potential to address these challenges by offering scalable, interactive, and personalized educational tools. This study evaluated the usability and accuracy of a Large Language Model-powered chatbot prototype under a Retrieval Augmented Generation framework designed to enhance oral cancer awareness, using a mixed-methods approach with six technical and clinical experts. Usability and accuracy were rated positively by 83.3% of the experts, with median scores of 6.65 and 7.67, respectively. Key areas for improvement included providing a clear introduction, simplifying the interface, addressing accessibility issues, and incorporating features like next-question suggestions, downloadable chats, and reference links. While content accuracy was well-received, gaps in conversational flow and technical term definitions were noted. These findings highlight the chatbot's potential to improve health literacy and reduce disparities.
口腔癌和口咽癌对美国黑人的影响尤为严重,由于晚期诊断和认知有限,导致了显著的医疗保健差距。人工智能驱动的聊天机器人有潜力通过提供可扩展、交互式和个性化的教育工具来应对这些挑战。本研究采用混合方法,与六位技术和临床专家合作,评估了在检索增强生成框架下设计的、用于提高口腔癌认知的大语言模型驱动的聊天机器人原型的可用性和准确性。83.3%的专家对可用性和准确性给予了积极评价,中位数分数分别为6.65和7.67。需要改进的关键领域包括提供清晰的介绍、简化界面、解决可访问性问题,以及纳入诸如下一个问题建议、可下载聊天记录和参考链接等功能。虽然内容准确性得到了认可,但在对话流程和技术术语定义方面存在差距。这些发现凸显了聊天机器人在提高健康素养和减少差距方面的潜力。