Interactive machine learning for health informatics: when do we need the human-in-the-loop? - Springer

Originally the term "machine learning" was defined as "... artificial generation of knowledge from experience," and the first studies have been performed with games, i.e., with the game of checkers [1]. Today, machine learning (ML) is the fastest growing technical field, at the intersection of informatics and statistics, tightly connected with data science and knowledge discovery, and health is among the greatest challenges [2, 3]. Particularly, probabilistic ML is extremely useful for health informatics, where most problems involve dealing with uncertainty. The theoretical basis for the probabilistic ML was laid by Thomas Bayes (1701–1761), [4, 5]. Probabilistic inference vastly influenced artificial intelligence and statistical learning and the inverse probability allows to infer unknowns, learn from data and make predictions [6, 7].