Applied machine learning and artificial intelligence in rheumatology

Abstract

Machine learning as a field of artificial intelligence is increasingly applied in medicine to assist patients and physicians. Growing datasets provide a sound basis with which to apply machine learning methods that learn from previous experiences. This review explains the basics of machine learning and its subfields of supervised learning, unsupervised learning, reinforcement learning and deep learning. We provide an overview of current machine learning applications in rheumatology, mainly supervised learning methods for e-diagnosis, disease detection and medical image analysis. In the future, machine learning will be likely to assist rheumatologists in predicting the course of the disease and identifying important disease factors. Even more interestingly, machine learning will probably be able to make treatment propositions and estimate their expected benefit (e.g. by reinforcement learning). Thus, in future, shared decision-making will not only include the patient's opinion and the rheumatologist's empirical and evidence-based experience, but it will also be influenced by machine-learned evidence.

Keywords: artificial intelligence; deep learning; machine learning; neural networks; rheumatology.

Fig. 1

Cognitive capabilities of artificial intelligence and types of machine learning

Fig. 2

Machine learning models use different function representations to map input features to certain outputs

Fig. 3

Difference of classification and regression models for disease prediction in RA

Fig. 4

Visualizations of fully connected neural networks

Fig. 5

Heatmap of a hand radiograph indicating regions of high attention for OA Courtesy of ImageBiopsy.

Fig. 6

Cycle of artificial intelligence-supported data management and clinical decision-making in rheumatology