Hafsa Habehh and Suril Gohel*
Recent advancements in artificial intelligence (AI) and machine learning (ML) technology have brought on substantial strides in predicting and identifying health emergencies, disease populations, and predicting disease state and immune response, amongst a few. Although skepticism remains regarding the practical application and interpretation of results from ML-based approaches in healthcare settings, the inclusion of these approaches is increasing at a rapid pace. Here we provide a brief overview of machine learning-based approaches and learning algorithms, including supervised, unsupervised and reinforcement learning, along with examples. Second, we discuss the application ML in several healthcare fields, including radiology, genetics, electronic health records, and neuroimaging. We also briefly discuss the risks and challenges of ML application to healthcare, such as system privacy and ethical concerns and provide suggestions for future applications.
Machine Learning, Healthcare, Support Vector Machine, EHR, Genomics
Department of Health Informatics, Rutgers University School of Health Professions 65 Bergen Street, Newark, NJ 07107, Department of Health Informatics, Rutgers University School of Health Professions 65 Bergen Street, Newark, NJ 07107