@Article{info:doi/10.2196/22841，作者=“刘陶然与曾，温黑与黄，凤秋与刘，爱莹与陈，闫辉与盛，洁与郭，易伟与Akinwunmi, Babatunde与Zhang, Casper JP与Ming Wai-Kit”，标题=“中国SARS-CoV-2大流行期间患者对人工智能应用与临床医生的疾病诊断偏好:离散选择实验”，期刊=“J Med Internet Res”，年=“2021”，月=“Feb”，日=“23”，卷=“23”，号=“2”，页=“e22841”，关键词=“离散选择实验”;人工智能;病人的偏好;多项逻辑分析;问卷调查;潜在类条件logit;应用程序;人类临床医师;诊断; COVID-19; China", abstract="Background: Misdiagnosis, arbitrary charges, annoying queues, and clinic waiting times among others are long-standing phenomena in the medical industry across the world. These factors can contribute to patient anxiety about misdiagnosis by clinicians. However, with the increasing growth in use of big data in biomedical and health care communities, the performance of artificial intelligence (Al) techniques of diagnosis is improving and can help avoid medical practice errors, including under the current circumstance of COVID-19. Objective: This study aims to visualize and measure patients' heterogeneous preferences from various angles of AI diagnosis versus clinicians in the context of the COVID-19 epidemic in China. We also aim to illustrate the different decision-making factors of the latent class of a discrete choice experiment (DCE) and prospects for the application of AI techniques in judgment and management during the pandemic of SARS-CoV-2 and in the future. Methods: A DCE approach was the main analysis method applied in this paper. Attributes from different dimensions were hypothesized: diagnostic method, outpatient waiting time, diagnosis time, accuracy, follow-up after diagnosis, and diagnostic expense. After that, a questionnaire is formed. 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. ", issn="1438-8871", doi="10.2196/22841", url="//www.mybigtv.com/2021/2/e22841", url="https://doi.org/10.2196/22841", url="http://www.ncbi.nlm.nih.gov/pubmed/33493130" }