  The severe acute respiratory syndrome coronavirus type-2 (SARS-CoV-2) caused
a global pandemic and immensely affected the global economy. Accurate,
cost-effective, and quick tests have proven substantial in identifying infected
people and mitigating the spread. Recently, multiple alternative platforms for
testing coronavirus disease 2019 (COVID-19) have been published that show high
agreement with current gold standard real-time polymerase chain reaction
(RT-PCR) results. These new methods do away with nasopharyngeal (NP) swabs,
eliminate the need for complicated reagents, and reduce the burden on RT-PCR
test reagent supply. In the present work, we have designed an artificial
intelligence-based (AI) testing method to provide confidence in the results.
Current AI applications for COVID-19 studies often lack a biological foundation
in the decision-making process, and our AI approach is one of the earliest to
leverage explainable AI (X-AI) algorithms for COVID-19 diagnosis using mass
spectrometry. Here, we have employed X-AI to explain the decision-making
process on a local (per-sample) and global (all samples) basis underscored by
biologically relevant features. We evaluated our technique with data extracted
from human gargle samples and achieved a testing accuracy of 94.12%. Such
techniques would strengthen the relationship between AI and clinical
diagnostics by providing biomedical researchers and healthcare workers with
trustworthy and, most importantly, explainable test results
