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Today, the US healthcare industry alone can save 300 B per year by using machine intelligence to analyze a rich set of existing medical data; results from these analyses can lead to breakthroughs such as more accurate medical diagnoses, discovery of new cures for diseases, and cost savings in the patient admission process at healthcare organizations. Because healthcare applications intrinsically imply a vast amount of data, the execution of any algorithm on medical data is computationally intensive. Significant advancements made in computational power in the past decade have provided the opportunity for many researchers to successfully implement various machine intelligence-based healthcare applications, which didn’t run efficiently on earlier computational platforms. In this paper, we provide a survey of machine intelligence algorithms within the context of healthcare applications; our survey includes a comprehensive list of the most commonly used computational models and algorithms. We view the application of these algorithms in multiple steps, namely, data acquisition, feature extraction, and aggregation, modeling, algorithm training, and algorithm execution and provide details—as well as representative case studies—for each step. We provide a set of metrics that are used to evaluate modeling and algorithmic performance, which facilitate the comparison of the presented models and algorithms. Medical cyber-physical systems are presented as an emerging application case study of machine intelligence in healthcare. We conclude our paper by providing a list of opportunities and challenges for incorporating machine intelligence in healthcare applications and provide an extensive list of tools and databases to help other researchers.
Shishvan et al. (Mon,) studied this question.