AI 中文总结
本研究通过受试者内实验评估AI在心血管疾病体育活动规划中的支持作用，发现AI作为选择性互补工具，其效果受可视化素养影响，并揭示验证、卸载、扩展和生成四种使用模式。
AI 中文摘要
自我追踪技术产生了纵向的患者生成健康数据，但将这些数据整合到临床决策中可能会增加信息处理负担。生成式AI可能支持意义建构，但其价值取决于临床背景和专业知识。我们研究了AI增强用于心血管疾病体育活动规划的临床决策支持系统。在一项平衡的受试者内研究中，26名运动生理学家在有无AI支持的情况下为四个真实心血管病例制定计划，随后对AI运动计划生成器进行了评估。AI总体上并未显著改善工作负荷、可用性、信心或计划质量；然而，其对计划质量的影响随着可视化素养的降低而增加，其对工作负荷的影响随着可视化素养的增加而增加。访谈和152次聊天机器人查询揭示了三种反复出现的用途：验证、卸载、扩展和生成。我们的研究结果将AI支持定位为对专业知识的互补性选择，同时强调了当临床医生恰恰在自己知识有限的领域寻求支持时存在的验证挑战。
英文摘要
Self-tracking technologies create longitudinal patient-generated health data, yet integrating these data into clinical decision-making can increase information-processing demands. Generative AI may support sensemaking, but its value depends on clinical context and expertise. We investigate AI augmentation of a clinical decision support system for physical-activity planning in cardiovascular disease. In a counterbalanced within-subjects study, 26 exercise physiologists developed plans for four real cardiovascular cases with and without AI support, followed by evaluation of an AI exercise-plan generator. AI did not significantly improve workload, usability, confidence, or plan quality overall; however, its effect on plan quality increased as visualization literacy decreased and its effect on workload increased as visualisation literacy increased. Interviews and 152 chatbot queries revealed three recurring uses: verifying, offloading, extend and generate. Our findings position AI support as a selective complement to professional expertise while highlighting validation challenges when clinicians seek support precisely where their own knowledge is limited.
