A Stepwise Approach to Analyzing Musculoskeletal Imaging Data With Artificial Intelligence

Abstract

The digitization of medical records and expanding electronic health records has created an era of “Big Data” with an abundance of available information ranging from clinical notes to imaging studies. In the field of rheumatology, medical imaging is used to guide both diagnosis and treatment of a wide variety of rheumatic conditions. Although there is an abundance of data to analyze, traditional methods of image analysis are human resource intensive. Fortunately, the growth of artificial intelligence may be a solution to handle large datasets. In particular, computer vision is a field within artificial intelligence that analyzes images and extracts information. Computer vision has impressive capabilities and can be applied to rheumatologic conditions, necessitating a need to understand how computer vision works. In this article, we provide an overview of artificial intelligence in rheumatology and conclude with a five step process to plan and conduct research in the field of computer vision. The five steps include: 1. Project Definition 2. Data Handling 3. Model Development 4. Performance Evaluation 5. Deployment into Clinical Care.This article is protected by copyright. All rights reserved.

Clinical evaluation is critical for the implementation of artificial intelligence in health care: comment on the article by Mickley et al

“…We were glad to read the review by Mickley et al, 1 which eloquently outlines the process of artificial intelligence (AI) development and implementation for medical imaging in rheumatology. We would suggest, however, that clinical evaluation warrants inclusion as a critical but often overlooked step in truly verifying safety and utility before use in practice.…”