Imaging Results in Data Usefully Analyzed by Artificial Intelligence Machine Learning.

Abstract

Many artificial intelligence (AI) machine learning (ML) papers focused on clinical outcomes use registry data inadequate for predictive modeling. In contrast, diagnostic imaging is an area where available information (pixels, etc.) can result in a reliable, clinically relevant, and accurate model. The use of deep learning for image analysis can reduce interobserver variability, and highlight subtle and meaningful features. AI augments, rather than replaces, clinical expertise, allowing faster, more consistent, and potentially more accurate diagnostic information. This is especially relevant when imaging data is abundant, as continuous model training can further refine diagnostic precision. An effective 3-step approach includes: 1) an efficient "detector" to determine where to look; 2) computational ability to focus on key features of the image and "blur out" background noise ("attention module"); and 3) interpreted key features ("explainability"). Next, the larger process of developing and employing a predictive model needs to be externally validated, to determine the extent to which these results will generalize outside of a single institution. Outside this setting, i.e., external validity, needs to be determined.