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Estimating average human performance has been performed inconsistently inresearch in diagnostic medicine. This has been particularly apparent in thefield of medical artificial intelligence, where humans are often comparedagainst AI models in multi-reader multi-case studies, and commonly reportedmetrics such as the pooled or average human sensitivity and specificity willsystematically underestimate the performance of human experts. We present theuse of summary receiver operating characteristic curve analysis, a techniquecommonly used in the meta-analysis of diagnostic test accuracy studies, as asensible and methodologically robust alternative. We describe the motivationfor using these methods and present results where we apply these meta-analytictechniques to a handful of prominent medical AI studies.

Docs are ROCs: A simple off-the-shelf approach for estimating average human performance in diagnostic studies