TY -非盟的林Senlin AU -李,力平盟——邹Haidong盟——徐,易盟- Lu,莉娜PY - 2022 DA - 2022/9/20 TI -医务人员和居民偏好使用深度学习在眼科疾病筛查:离散选择试验乔- J地中海互联网Res SP - e40249六世- 24 - 9千瓦,离散选择试验KW -偏好KW -人工智能KW - AI KW -视觉健康KW -筛选AB -背景:深度学习辅助眼病诊断技术在眼病筛查中的应用越来越广泛。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。共有39名医务人员和318名住院医师回答问卷，并对不同属性的筛查策略进行权衡，包括漏诊率、超诊率、筛查结果反馈效率、眼科医生参与程度、组织形式、成本、筛查结果反馈形式。采用条件logit模型和逐步选择方法估计偏好。结果:医护人员首选准确性较高的深度学习模型，其特异性应大于90%(10%超诊断的比值比[OR]=0.61;P<.001)，远远高于食品和药物管理局的标准。 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. SN - 1438-8871 UR - //www.mybigtv.com/2022/9/e40249 UR - https://doi.org/10.2196/40249 UR - http://www.ncbi.nlm.nih.gov/pubmed/36125854 DO - 10.2196/40249 ID - info:doi/10.2196/40249 ER -