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Online COVID-19 Symptom Checkers: Comparison Study
Nicolas Munsch;
Alistair Martin;
Stefanie Gruarin;
Jama Nateqi;
Isselmou Abdarahmane;
Bernhard Knapp
ABSTRACT
Background:
A large number of online COVID-19 symptom checkers and chatbots have been developed but anecdotal evidence suggests that their conclusions are highly variable. To our knowledge, no study has evaluated the accuracy of COVID-19 symptom checkers in a statistically rigorous manner.
Objective:
The aim of this study is to provide diagnostic accuracy evaluations of online COVID-19 symptom checkers.
Methods:
We evaluated 10 different COVID-19 symptom checkers screening 50 COVID-19 case reports alongside 410 non-COVID-19 control cases.
Results:
The symptom checker Symptoma performs best (F1=0.92), followed by Infermedica (F1=0.80), Cleveland Clinic (F1=0.76), Providence (F1=0.75), Your.MD (F1=0.72), CDC (F1=0.71), Babylon (F1=0.70), Apple (F1=0.70), Ada (F1=0.42) and Docyet (F1=0.27).
Conclusions:
We find that the number of correctly assessed cases varies considerably between different symptom checkers, with Symptoma showing the overall best performance.
Citation
Please cite as:
Munsch N, Martin A, Gruarin S, Nateqi J, Abdarahmane I, Knapp B
Diagnostic Accuracy of Web-Based COVID-19 Symptom Checkers: Comparison Study