SP3 Should I Choose Artificial Intelligence or Clinicians' Diagnosis? a Discrete Choice Experiment of Patients' Preference UNDER COVID-19 Pandemic in China

Objectives: This study aims to quantify the strength of patients’ heterogeneous preferences for various aspects of Artificial intelligence (AI) diagnosis versus clinicians under the epidemic of COVID-19 in china Moreover, to illustrate the different decision-making factors of the latent class of discrete choice experiment (DCE) and prospects for application of AI techniques in diagnosis treatment in the pandemic of SARS-CoV-2 and future Methods: A DCE approach was used Attributes from different dimensions which have been hypothesized were diagnostic method;waiting periods;diagnosis time;the precision of diagnosis;Follow-up support service, and diagnostic expenses With data from the DCE component, a restricted latent class model was estimated with fixed size of each class to determine discrete ‘classes’ of diagnosis preferences For statistical analysis, we construct generalized logit and mixed logit models with the 428 datasets Results: 55 74 per cent of the respondents are opted for AI diagnosis regardless of the description of the clinicians Logit models presented the evident patients\u0027 preference of \u0027AI+clinician\u0027 diagnosis method And clearly, the higher accuracy is, the more patients would prefer In addition, the most acceptable latent class model is consisting of three latent classes of respondents, defined by different preferences for the diagnosis cost, time, method, waiting time and accuracy Attributes with the most substantial effect on choices were the accuracy and payments, especially the preferences for diagnosis ‘accuracy’ attribute, was constant across classes Except for ‘class 1’, in which people highlight the diagnosis methods All attributes had a significant effect on choices in the expected direction Conclusions: Considerable segments of respondents had fixed preferences for either diagnosis option Applying latent class analysis was essential in quantifying preferences for attributes of diagnosis choice People’s preference to the “Accuracy” was palpable AI will have a potential market, however, accuracy and diagnosis expense are needed to be taken into consideration