中国SARS-CoV-2大流行期间患者对人工智能应用与临床医生疾病诊断卡塔尔世界杯8强波胆分析的偏好:离散选择实验%刘a，曾陶然%，黄永黑%，刘凤秋%，刘爱英%，陈爱英%，陈艳慧%，盛洁%，郭洁%，一伟%，Akinwunmi,Babatunde %， Zhang,Casper JP %， Ming - wei - kit %+暨南大学医学院公共卫生与预防医学系，广州黄埔西路601号，510000，中国，86 14715485116wkming@connect.hku.hk %K离散选择实验%K人工智能%K患者偏好%K多项logit分析%K问卷%K潜在类条件logit %K app %K人类临床医生%K诊断%K COVID-19 %K中国%D 2021 %7 23.2.2021 %9背景:误诊、任意收费、恼人的排队、诊所等待时间等是世界各地医疗行业长期存在的现象。这些因素可能导致患者对临床医生的误诊感到焦虑。然而，随着大数据在生物医学和卫生保健领域的应用越来越多，人工智能(Al)诊断技术的性能正在提高，可以帮助避免医疗实践错误，包括在当前COVID-19的情况下。目的:本研究旨在从中国新冠肺炎疫情背景下人工智能诊断与临床医生的不同角度，可视化和衡量患者的异质性偏好。我们还旨在说明离散选择实验(DCE)潜在类别的不同决策因素，以及人工智能技术在SARS-CoV-2大流行期间和未来判断和管理中的应用前景。方法:以DCE方法为主要分析方法。假设不同维度的属性:诊断方法、门诊候诊时间、诊断时间、准确性、诊断后随访、诊断费用。然后，形成一份调查问卷。 With collected data from the DCE questionnaire, we apply Sawtooth software to construct a generalized multinomial logit (GMNL) model, mixed logit model, and latent class model with the data sets. Moreover, we calculate the variables’ coefficients, standard error, P value, and odds ratio (OR) and form a utility report to present the importance and weighted percentage of attributes. Results: A total of 55.8% of the respondents (428 out of 767) opted for AI diagnosis regardless of the description of the clinicians. In the GMNL model, we found that people prefer the 100% accuracy level the most (OR 4.548, 95% CI 4.048-5.110, P<.001). For the latent class model, the most acceptable model consists of 3 latent classes of respondents. The attributes with the most substantial effects and highest percentage weights are the accuracy (39.29% in general) and expense of diagnosis (21.69% in general), especially the preferences for the diagnosis “accuracy” attribute, which is constant across classes. For class 1 and class 3, people prefer the AI + clinicians method (class 1: OR 1.247, 95% CI 1.036-1.463, P<.001; class 3: OR 1.958, 95% CI 1.769-2.167, P<.001). For class 2, people prefer the AI method (OR 1.546, 95% CI 0.883-2.707, P=.37). The OR of levels of attributes increases with the increase of accuracy across all classes. Conclusions: Latent class analysis was prominent and useful in quantifying preferences for attributes of diagnosis choice. People’s preferences for the “accuracy” and “diagnostic expenses” attributes are palpable. AI will have a potential market. However, accuracy and diagnosis expenses need to be taken into consideration. %M 33493130 %R 10.2196/22841 %U //www.mybigtv.com/2021/2/e22841 %U https://doi.org/10.2196/22841 %U http://www.ncbi.nlm.nih.gov/pubmed/33493130