The recent development in the areas of deep learning and deep convolutional neural networks has significantly progressed and advanced the field of computer vision (CV) and network data analysis and understanding. Complex tasks such as classifying and segmenting medical images and localising and recognising objects of interest have become much less challenging. This progress has the potential of accelerating research and deployment of multitudes of medical applications that utilise CV. However, in reality, there are limited practical examples being physically deployed into front-line health facilities. In this paper, we examine the current state of the art in CV as applied to the medical domain. We discuss the main challenges in CV and intelligent data-driven medical applications and suggest future directions to accelerate research, development, and deployment of CV applications in health practices. During the last few years computer applications have undergone a dramatic transformation from simple data processing to machine learning, thanks to the availability and accessibility of huge volumes of data collected through sensors and the internet. The idea of machine learning demonstrates and propagates the fact that the computer has the ability to improve itself with the passage of time. The western countries have shown great interest on the topic of machine learning, computer vision, and pattern recognition via organizing conferences, workshops, collective discussion, experimentation, and real-life implementation. This study on machine learning and computer vision explores and analytically evaluates the machine learning applications in computer vision and predicts future prospects. The study has found that the machine learning strategies in computer vision are supervised, un-supervised, and semi- supervised. The commonly used algorithms are neural networks, k-means clustering, and support vector machines. The most recent applications of machine learning in computer vision are object detection, object classification, and extraction of relevant information from images, graphic documents, and videos. Additionally, Tensor flow, Faster- RCNN-Inception-V2 model, and Anaconda software development environment are used to identify cars and persons in images.