Rationale
Assessing the performance of diagnostic tests requires evaluation of the amount of diagnostic uncertainty the test reduces (i.e. 0% - useless test, 100% - perfect test). Statistical measures currently dominating the evidence-based medicine (EBM) field and particularly meta-analysis (e.g. sensitivity and specificity), cannot explicitly measure this uncertainty reduction. Mutual information (MI), an information theory statistic, is a more appropriate metric for evaluating diagnostic tests as it explicitly quantifies uncertainty and, therefore, facilitates natural interpretation of a test’s value. In this paper, we propose the use of MI as a single measure to express diagnostic test performance and demonstrate how it can be used in meta-analysis of diagnostic test studies.