Many applications of whole slide imaging technology in the field of pathology have been verified since it was proposed in 1999. In recent years, the application of artificial intelligence (AI) technology in the field of medical diagnosis has not only provided new ideas for improving healthcare, but also set off a new wave of research in the field of tumor pathology. As an emerging means of AI implementation, deep learning (DL) shows great potential in the analysis of histopathological images, such as the detection, classification, metastasis, and prognosis prediction of tumors, in the context of big data and digital microscopy technology. Traditional pathological diagnosis results are affected by the pathologists’ personal knowledge, clinical experience, and logical way of thinking; such results are subjective and have low repetition rate. As a new technology, AI can help pathologists in making pathological diagnosis, avoid the above mentioned issues to a certain extent, reduce artificial errors, improve the accuracy and repetition rate of pathological diagnosis, and support real-time diagnosis decision. These advantages can not only ease the pressure on medical and health resources, but also support precision medicine. This article reviews the status quo, opportunities and challenges of DL application in pathological image analysis of lung cancer, breast cancer and prostate cancer, and discusses the problems in model development and application supervision from the perspective of pathologists.