@文章{info:doi/ 10.21960 /40249，作者=“林森林和李，黎萍和邹海东和徐，易和鲁，莉娜”，标题=“深度学习在眼病筛查中的医护人员和住院医师偏好:离散选择实验”，期刊=《J医学互联网研究》，年=“2022”，月=“9”，日=“20”，卷=“24”，数=“9”，页=“e40249”，关键词=“离散选择实验;偏好;人工智能;人工智能;视力健康;背景:深度学习辅助眼病诊断技术在眼病筛查中的应用日益广泛。然而，目前还没有研究表明医疗服务提供者和居民愿意使用它的先决条件。目的:本文旨在揭示医疗服务提供者和居民对使用人工智能(AI)进行社区眼病筛查的偏好，特别是对准确性的偏好。方法:采用离散选择实验方法，对上海市卫生保健机构和居民进行问卷调查。共有34家具有充分人工智能辅助筛查经验的医疗机构参与。 A total of 39 medical staff and 318 residents were asked to answer the questionnaire and make a trade-off among alternative screening strategies with different attributes, including missed diagnosis rate, overdiagnosis rate, screening result feedback efficiency, level of ophthalmologist involvement, organizational form, cost, and screening result feedback form. Conditional logit models with the stepwise selection method were used to estimate the preferences. Results: Medical staff preferred high accuracy: The specificity of deep learning models should be more than 90{\%} (odds ratio [OR]=0.61 for 10{\%} overdiagnosis; P<.001), which was much higher than the Food and Drug Administration standards. However, accuracy was not the residents' preference. Rather, they preferred to have the doctors involved in the screening process. In addition, when compared with a fully manual diagnosis, AI technology was more favored by the medical staff (OR=2.08 for semiautomated AI model and OR=2.39 for fully automated AI model; P<.001), while the residents were in disfavor of the AI technology without doctors' supervision (OR=0.24; P<.001). Conclusions: Deep learning model under doctors' supervision is strongly recommended, and the specificity of the model should be more than 90{\%}. In addition, digital transformation should help medical staff move away from heavy and repetitive work and spend more time on communicating with residents. ", issn="1438-8871", doi="10.2196/40249", url="//www.mybigtv.com/2022/9/e40249", url="https://doi.org/10.2196/40249", url="http://www.ncbi.nlm.nih.gov/pubmed/36125854" }