Fig. 2: Results of the AI models on the test set.

a, Comparison of the ROC curves for the joint model, the CNN model trained on the basis of CT images, the MLP model trained on the basis of clinical information and two radiologists. b, Comparison of success rates of diagnosing patients who are positive for COVID-19 with normal CT scans. Radiologists were provided with both CT imaging and clinical information in making their diagnoses. c–e, Comparison of the AUCs (c), sensitivities (d) and specificities (e) achieved by the AI models and radiologists. Two-sided P values were calculated by comparing the joint model to the CNN model, the MLP model and the two human readers in sensitivity, specificity and AUC. AUC comparisons were evaluated by the DeLong test; sensitivity and specificity comparisons were calculated by using the exact Clopper–Pearson method to compute the 95% CI shown in parentheses and exact McNemar’s test to calculate the P value.