Artificial Intelligence and Machine Learning: What You Always Wanted to Know but Were Afraid to Ask

Abstract

The access to increasing volumes of scientific and clinical data, particularly with the implementation of electronic health records, has reignited an enthusiasm for artificial intelligence and its application to the health sciences. This interest has reached a crescendo in the past few years with the development of several machine learning– and deep learning–based medical technologies. The impact on research and clinical practice within gastroenterology and hepatology has already been significant, but the near future promises only further integration of artificial intelligence and machine learning into this field. The concepts underlying artificial intelligence and machine learning initially seem intimidating, but with increasing familiarity, they will become essential skills in every clinician's toolkit. In this review, we provide a guide to the fundamentals of machine learning, a concentrated area of study within artificial intelligence that has been built on a foundation of classical statistics. The most common machine learning methodologies, including those involving deep learning, are also described.

Keywords

• Big Data
• Deep Learning
• Neural Networks
• Statistical Learning
• Supervised Learning