As of 2017, the problem of "computer vision" meant that the problem of finding patterns in an image can be considered as solved, and this problem has an impact on our lives. For instance:

•  The paper Dermatologist-level classification of skin cancer with deep neural networks, Andre Esteva, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau & Sebastian Thrun, 2017 https://www.nature.com/nature/journal/v542/n7639/full/nature21056.html trains a CNN using a dataset of 129,450 clinical images consisting of 2,032 different diseases. They test the results against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists.
• The paper High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks, Krzysztof J. Geras, Stacey Wolfson, S. Gene Kim, Linda Moy, Kyunghyun Cho, https://arxiv.org/abs/1703.07047 promises to improve the breast cancer screening process through its innovative architecture, which can handle the four standard views, or angles, without sacrificing a high resolution. As opposed to the commonly used DCN architectures for natural images, which work with images of 224 x 224 pixels, the MV-DCN is also capable of using a resolution of 2600 x 2000 pixels.