In vivo reflectance confocal microscopy (RCM) enables clinicians to examine lesions’ morphological and cytological information in epidermal and dermal layers, while reducing the need for biopsies. As RCM is being adopted more widely, the workflow is expanding from real-time diagnosis at the bedside to include a “capture, store and forward” model with image interpretation and diagnosis occurring offsite, similar to radiology. As the patient may no longer be present at the time of image interpretation, quality assurance is key during image acquisition. Herein, we introduce a quality assurance process by means of automatically quantifying diagnostically uninformative areas within the lesional area, by using RCM and co-registered dermoscopy images together. We trained and validated a pixel-level segmentation model on 117 RCM mosaics collected by international collaborators. The model delineates diagnostically uninformative areas with 82% sensitivity and 93% specificity. We further tested the model on a separate set of 372 co-registered RCM-dermoscopic image pairs and illustrate how the results of the RCM only model can be improved via a multi-modal (RCM + Dermoscopy) approach, which can help quantify the uninformative regions within the lesional area. Our data suggest that machine learning based automatic quantification offers a feasible objective quality control measure for RCM imaging.