Enhancing Readability of Lay Abstracts and Summaries for Medical Knowledge Using Generative Artificial Intelligence: A Randomized Controlled Trial (BRIDGE AI 3)
试验速览
- 阶段
- 2 期
- 状态
- 已完成
- 入组人数
- 120
- 试验地点
- 1
- 主要终点
- Readability Change
研究概览
简要总结
This trial tests if AI can help make medical info clear and readable. Many patients struggle to find medical informations that easy to read and understand from verified medical sources. The study tests if an AI tool can assist health providers to craft clear text for patients more fast than what they do now. Health providers are split at random into two groups-one uses the AI tool and one does not. The trial tests how clear the text is, how correct it is, and how much time is saved. The aim is to see if AI can close the gap between complex research and what patients can grasp.
详细描述
This study evaluates whether a generative artificial intelligence (AI) tool can improve the readability and accessibility of lay summaries derived from scientific medical abstracts. Many patients encounter difficulty understanding medical literature due to technical language and complexity, which can limit informed decision-making and engagement with healthcare information.
The BRIDGE-AI (Provider Perspective) initiative aims to address this gap by enabling healthcare professionals and researchers to generate patient-friendly summaries of scientific content using AI-assisted tools. The intervention leverages a generative AI framework (pub2people) designed to translate complex medical terminology into language that is understandable to a general audience.
In this randomized controlled study, participants with experience in scientific publishing will be assigned to either an AI-assisted group or a control group using conventional methods. Participants will be asked to transform scientific abstracts into layperson-friendly summaries. The study compares AI-assisted and manually generated outputs in terms of readability, accuracy, and efficiency.
The primary objective is to determine whether AI-assisted generation improves the readability of lay summaries compared to standard approaches. Secondary objectives include evaluating the accuracy of AI-generated summaries relative to source material and assessing potential time savings associated with AI use.
This study contributes to ongoing efforts to improve health communication by evaluating scalable tools that may enhance the translation of complex medical information into patient-accessible formats.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Health Services Research
- 盲法
- Single (Participant)
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Provider Participants
- •Corresponding authors who have been published in the top 10 journals of urology and medicine
- •All genders
- •Any profession
- •18+ years of age
排除标准
- •Anyone under the age of 18
- •Participants that have not published in the top 10 journals of urology and medicine
研究组 & 干预措施
Gold Standard
Pub2Post
干预措施: Pub2Post (Other)
结局指标
主要结局
Readability Change
时间窗: The assessment will be conducted immediately after the study closes, which will occur 4 weeks after enrollment.
Flesch Reading Ease Score Description: Measures text readability based on sentence length and word syllables. Scale: 0 to 100 Interpretation: Higher scores indicate easier readability (better outcome).
次要结局
- Time Saving(The assessment will be conducted immediately after the study closes, which will occur 4 weeks after enrollment.)
- Correctness and meaning retention(The assessment will be conducted immediately after the study closes, which will occur 4 weeks after enrollment.)
- Perceived Task Difficulty(The assessment will be conducted immediately after the study closes, which will occur 4 weeks after enrollment)
- Perceived Task Duration(The assessment will be conducted immediately after the study closes, which will occur 4 weeks after enrollment)
- Perceived Helpfulness of the intervention(The assessment will be conducted immediately after the study closes, which will occur 4 weeks after enrollment)
- System Usability Scale (SUS) Score(Immediately after completing the system/task (post-use assessment))
- Perceived Usefulness (Technology Acceptance Model)(Immediately after completing the system/task (post-use assessment))
- Perceived Ease of Use (Technology Acceptance Model)(Immediately after completing the system/task (post-use assessment))
研究者
Giovanni Cacciamani
Associate Professor of Urology
University of Southern California