Doshi Rushabh H, Amin Kanhai, Chan Shin Mei, Kaur Manroop, Bajaj Simar S, Khosla Pavan, Kothari Veer T, Mozayan Ali, Tocino Irena, Chheang Sophie
Yale School of Medicine, New Haven, Connecticut, United States of America.
Yale College, New Haven, Connecticut, United States of America.
PLoS One. 2025 Sep 3;20(9):e0331368. doi: 10.1371/journal.pone.0331368. eCollection 2025.
Radiology reports are an integral part of patient medical records; however, these reports often contain complex medical terminology that are difficult for patients to comprehend, potentially leading to anxiety, misunderstanding, and misinterpretation. The development of user-friendly instruments to improve understanding is thus critically important to enhance health literacy and empower patients. In this study, we introduce a novel artificial intelligence (AI) interface, the Rads-Lit Tool, which can simplify radiology reports for patients using natural language processing (NLP) techniques. This manuscript presents the development process, methodology, and results of the Rads-Lit Tool, demonstrating its potential to simplify radiology reports across various examination types and complexity levels. Our findings highlight that patient-facing AI-driven tools can enhance patient health literacy and foster improved patient-provider communication in radiology.
放射学报告是患者病历的重要组成部分；然而，这些报告通常包含复杂的医学术语，患者难以理解，这可能会导致焦虑、误解和错误解读。因此，开发便于用户理解的工具对于提高健康素养和增强患者能力至关重要。在本研究中，我们引入了一种新型人工智能（AI）界面——放射学素养工具（Rads-Lit Tool），它可以使用自然语言处理（NLP）技术为患者简化放射学报告。本文介绍了Rads-Lit Tool的开发过程、方法和结果，展示了其在简化各种检查类型和复杂程度的放射学报告方面的潜力。我们的研究结果表明，面向患者的人工智能驱动工具可以提高患者的健康素养，并促进放射学领域患者与医疗服务提供者之间更好的沟通。
