医学大数据主要包括电子健康档案数据（electronic health record，EHR）、医学影像数据和基因信息数据等，其中医学影像数据占现阶段医学数据的绝大部分。如何将医学大数据应用于临床实践?这是计算机科学研究人员非常关注的问题，医学人工智能提供了一个很好的答案。通过结合医学图像大数据分析方向截至2020年的最新研究进展，以及医学图像大数据分析领域最近的工作，梳理了当前在医学图像领域以核磁共振影像、超声影像、病理和电信号为代表的4个子领域以及部分其他方向使用深度学习进行图像分析的方法理论和主要流程，对不同算法进行结果评价。本文分析了现有算法的优缺点以及医学影像领域的重难点，介绍了智能成像和深度学习在大数据分析以及疾病早期诊断领域的应用，同时展望了本领域未来的发展热点。深度学习在医学影像领域发展迅速，发展前景广阔，对疾病的早期诊断有重要作用，能有效提高医生工作效率并减轻负担，具有重要的理论研究和实际应用价值。

Medical big data mainly include electronic health record data, such as medical imaging data and genetic information data, among which medical imaging data takes up the most of medical data currently. One of the problems that researchers in computer science are greatly concerned about is how to apply medical big data in clinical practice.Artificial intelligence (AI) provides a good way to address this problem. AI algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Historically, in radiology practice, trained physicians visually assess medical images for the detection, characterization, and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. In this review, by combining recent work and the latest research progress of big data analysis of medical images until 2020, we have summarized the theory,main process, and evaluation results of multiple deep learning algorithms in some fields of medical image analysis, including magnetic resonance imaging (MRI), pathology imaging, ultrasound imaging, electrical signals, digital radiography, molybdenum target, and diabetic eye imaging, using deep learning. MRI is one of the main research areas of medical image analysis. The existing research literature includes Alzheimer's disease MRI, Parkinson's disease MRI, brain tumor MRI, prostate cancer MRI, and cardiac MRI. MRI is also divided into two-dimensional and three-dimensional image analysis, especially for three-dimensional data, where insufficient data volume leads to problems such as overfitting, large calculations, and slow training.Medical ultrasound (also known as diagnostic sonography or ultrasonography) is a diagnostic imaging technique or therapeutic application of ultrasound. It is used to create an image of internal body structures such as tendons, muscles, joints, blood vessels, and internal organs. It aims to find the source of a disease or to exclude pathology. The practice of examining pregnant women using ultrasound is called obstetric ultrasonography and was an early development and application of clinical ultrasonography.Ultrasonography uses sound waves with higher frequencies than those audible to humans (>20 000 Hz). Ultrasonic images, also known as sonograms, are made by sending ultrasound pulses into the tissue using a probe. The ultrasound pulses echo off tissues with different reflection properties and are recorded and displayed as an image.Many different types of images can be formed. The most common is a B-mode image (brightness), which displays the acoustic impedance of a two-dimensional cross-section of a tissue. Other types can display blood flow, tissue motion over time, the location of blood, the presence of specific molecules, the stiffness of a tissue, or the anatomy of a three-dimensional region. Pathology is the gold standard for diagnosing some diseases, especially digital image of pathology.We specifically discuss AI combined with digital pathology images for diagnosis.Electroencephalography (EEG) is an electrophysiological monitoring method to record the electrical activity of the brain. It is typically noninvasive, with the electrodes placed along the scalp.However, invasive electrodes are sometimes used, for example in electrocorticography, sometimes called intracranial EEG. EEG is most often used to diagnose epilepsy, which causes abnormalities in EEG readings. It is also used to diagnose sleep disorders, depth of anesthesia, coma, encephalopathies, and brain death. EEG used to be a first-line method of diagnosis for tumors, stroke, and other focal brain disorders, but its use has decreased with the advent of high-resolution anatomical imaging techniques such as MRI and computed tomography (CT). Despite limited spatial resolution, EEG continues to be a valuable tool for research and diagnosis. It is one of the few mobile techniques available and offers millisecond-range temporal resolution, which is not possible with CT, positron emission tomography (PET), or MRI.Electrocardiography(ECG or EKG) is the process of producing an electrocardiogram. It is a graph of voltage versus time of the electrical activity of the heart using electrodes placed on the skin. These electrodes detect small electrical changes that are a consequence of cardiac muscle depolarization followed by repolarization during each cardiac cycle (heartbeat). Changes in the normal ECG pattern occur in numerous cardiac abnormalities, including cardiac rhythm disturbances (e.g., atrial fibrillation and ventricular tachycardia), inadequate coronary artery blood flow (e.g., myocardial ischemia and myocardial infarction), and electrolyte disturbances (e.g., hypokalemia and hyperkalemia).We analyzed the advantages and disadvantages of existing algorithms and the important and difficult points in the field of medical imaging, and introduced the application of intelligent imaging and deep learning in the field of big data analysis and early disease diagnosis. The current algorithms in the field of medical imaging have made considerable progress, but there is still a lot of room for development. We also focus on the optimization and improvement of different algorithms in different sub-fields under a variety of segmentation and classification indicators (e.g., Dice, IoU, accuracy and recall rate), and we look forward to the future development hotspots in this field. Deep learning has developed rapidly in the field of medical imaging and has broad prospects for development. It plays an important role in the early diagnosis of diseases. It can effectively improve the work efficiency of doctors and reduce their burden. Moreover, it has important theoretical research and practical application value.