In a medical diagnostic ML model used to detect a disease, a Type II error corresponds to which outcome in the confusion matrix?

In statistical hypothesis testing terms, a Type I error is "rejecting a true null hypothesis" (a false alarm), while a Type II error is "failing to reject a false null hypothesis" (a missed detection). Mapped onto a diagnostic classifier where the null hypothesis is typically "patient is healthy," a Type II error occurs when the model fails to flag an actually diseased patient — i.e., it predicts "healthy" (negative) when the true condition is "diseased" (positive). This is exactly a False Negative. A Type I error, by contrast, corresponds to a False Positive.