Background Artificial intelligence (AI) methods can potentially be used to relieve the pressure that the COVID-19 pandemic has exerted on public health. In cases of medical resource shortages caused by the pandemic, changes in people’s preferences for AI clinicians and traditional clinicians are worth exploring. Objective We aimed to quantify and compare people’s preferences for AI clinicians and traditional clinicians before and during the COVID-19 pandemic, and to assess whether people’s preferences were affected by the pressure of pandemic. Methods We used the propensity score matching method to match two different groups of respondents with similar demographic characteristics. Respondents were recruited in 2017 and 2020. A total of 2048 respondents (2017: n=1520; 2020: n=528) completed the questionnaire and were included in the analysis. Multinomial logit models and latent class models were used to assess people’s preferences for different diagnosis methods. Results In total, 84.7% (1115/1317) of respondents in the 2017 group and 91.3% (482/528) of respondents in the 2020 group were confident that AI diagnosis methods would outperform human clinician diagnosis methods in the future. Both groups of matched respondents believed that the most important attribute of diagnosis was accuracy, and they preferred to receive combined diagnoses from both AI and human clinicians (2017: odds ratio [OR] 1.645, 95% CI 1.535-1.763; P<.001; 2020: OR 1.513, 95% CI 1.413-1.621; P<.001; reference: clinician diagnoses). The latent class model identified three classes with different attribute priorities. In class 1, preferences for combined diagnoses and accuracy remained constant in 2017 and 2020, and high accuracy (eg, 100% accuracy in 2017: OR 1.357, 95% CI 1.164-1.581) was preferred. In class 2, the matched data from 2017 were similar to those from 2020; combined diagnoses from both AI and human clinicians (2017: OR 1.204, 95% CI 1.039-1.394; P=.011; 2020: OR 2.009, 95% CI 1.826-2.211; P<.001; reference: clinician diagnoses) and an outpatient waiting time of 20 minutes (2017: OR 1.349, 95% CI 1.065-1.708; P<.001; 2020: OR 1.488, 95% CI 1.287-1.721; P<.001; reference: 0 minutes) were consistently preferred. In class 3, the respondents in the 2017 and 2020 groups preferred different diagnosis methods; respondents in the 2017 group preferred clinician diagnoses, whereas respondents in the 2020 group preferred AI diagnoses. In the latent class, which was stratified according to sex, all male and female respondents in the 2017 and 2020 groups believed that accuracy was the most important attribute of diagnosis. Conclusions Individuals’ preferences for receiving clinical diagnoses from AI and human clinicians were generally unaffected by the pandemic. Respondents believed that accuracy and expense were the most important attributes of diagnosis. These findings can be used to guide policies that are relevant to the development of AI-based health care.

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BACKGROUND The COVID-19 pandemic poses a great threat to the public health system globally and has squeezed medical and doctor resources. Artificial intelligence (AI) has potential uses in virus detection and relieving the public health pressure caused by the pandemic. In the case of a shortage of medical resources caused by the pandemic, whether people’s preference for AI doctors and traditional clinicians has changed is worth exploring. OBJECTIVE We aim to quantify and compare people’s preference for AI medicine and traditional clinicians before and after the COVID-19 pandemic to check whether people’s preference is affected by the pressure of pandemic METHODS The propensity score matching (PSM) method was applied to match two different groups of respondents recruited in 2017 and 2020 with similar demographic characteristics. A total of 2048 respondents (1520 from 2017 and 528 from 2020) completed the questionnaire and were included in the analysis. The Multinomial Logit Model (MNL) and Latent Class Model (LCM) were used to explore people’s preferences for different diagnosis methods. RESULTS Among these respondents, 84.7% in 2017 and 91.3% in 2020 were confident that AI diagnosis would outperform human clinician diagnoses in the future. Both groups of respondents matched from 2017 and 2020 attached most importance to the attribute ‘accuracy’, followed by ‘diagnosis expense’, and they prefer the combined diagnosis of AI and human clinicians (2017: odds ratio [OR] 1.645; 95% CI 1.535,1.763, p < 0.001; 2020: OR 1.513, 95% CI 1.413, 1.621, p < 0.001, Reference level: Clinician). LCM identified three classes with different attribute priorities. In Class 1, the preference for combination diagnosis and accuracy remains constant in 2017 and 2020, and higher accuracy (e.g., 2017 OR for 100% 1.357; 95% CI 1.164, 1.581) is preferred. People in 2017 and 2020 prefer 0 min outpatient waiting time and 0 RMB diagnosis expense. In Class 2, the 2017 matched data is also very similar to class 2 in 2020, AI combined with human clinicians (2017: OR 1.204, 95% CI 1.039, 1.394, p = 0.011; 2020: OR 2.009, 95% CI 1.826, 2.211, p < 0.001, Reference level: Clinician) and 20 minutes (2017: OR 1.349, 95% CI 1.065, 1.708, p < 0.001; 2020: OR 1.488, 95% CI 1.287, 1.721, p < 0.001, Reference level, 0 min) of outpatient waiting time were consistently preferred. In Class 3, the respondents in 2017 and 2020 had different preferences for diagnosis method; respondents in Class 3 of 2017 prefer clinicians, whereas respondents in Class 3 of 2020 prefer AI diagnosis. The odds ratios of accuracy continued increasing with the increasing of accuracy, like other classes of 2017 and 2020. As for the latent class segmented according to different sexes, all of the male and female respondent classes from 2017 and 2020 rank accuracy as the most important attribute. CONCLUSIONS Individual preference for clinical diagnosis between AI and human clinicians were very similar and mostly unaffected by the burden of the public health system caused by the pandemic. Diagnosis accuracy and expense for diagnosis were of the most important attributes of choice of the type of diagnosis. These findings can provide guidance for policymaking relevant to the development of AI-based healthcare.

BACKGROUND The listed pharmaceutical industry of China is growing swiftly by about 10% interest per year. However, risk always keeps pace with improvements. In China, listed companies with significant financial distress or irregularities will be assigned a special treatment (ST) label indicating a risk warning. Recently, eight listed pharmaceutical companies with a ST sign are surviving with but present serious investment risks. OBJECTIVE This paper aimed to discover the most significant factors that cause conversion of a listed pharmaceutical company into an ST firm. Tailored approaches for protecting China’s listed pharmaceutical companies from financial risks are being developed in order to help this domain in profit. Besides, we also aimed to offer suggestions for investors for investigating Chinese listed pharmaceuticals with the goal of assisting the investors in making successful investments. METHODS After collecting data from online databases, a principal component analysis (PCA) model was applied for descending data dimensions. After selecting the components with highest contribution, a logistic regression (LR) model was conducted for simplifying the outcome and calculating the intercept, component coefficients, standard error (SE), and Z and p-values. RESULTS Nine principal components were crucial from the principal component analysis (PCA) model, and two components (components 1 and 5) remaining as the most important factors after the LR model. The estimated intercept was 4.866 (SE 1.096, Z-value 4.442, p < 0.001). The estimated coefficient for components 1 (SE 0.332, Z-value 3.067, p = 0.002) and 5 (SE 0.643, Z-value −2.6, p = 0.009) were 1.017 and −1.672, respectively. CONCLUSIONS Investors are supposed to supervise the accounting conditions in three sectors: (1) solvency, profitability, and research and development (R&D) investment; (2) running the firm properly; and/or (3) investing successfully. Firms are supposed to hire professional partitioners as leaders. The major shareholders should not plan any questionable investments for personal income, and they should ensure the firm works under conditions with low liability, high profitability, and R&D costs that match the perfect growth opportunity after the Coronavirus 2019 (COVID-19) pandemic and the strong growth of China’s economy in 2020.