Integrating AI into Radiology workflow: Levels of research, production, and feedback maturity [article]

Engin Dikici, Matthew Bigelow, Luciano M. Prevedello, Richard D. White, Barbaros Selnur Erdal
2019 arXiv   pre-print
This report represents a roadmap for integrating Artificial Intelligence (AI)-based image analysis algorithms into existing Radiology workflows such that: (1) radiologists can significantly benefit from enhanced automation in various imaging tasks due to AI; and (2) radiologists' feedback is utilized to further improve the AI application. This is achieved by establishing three maturity levels where: (1) research enables the visualization of AI-based results/annotations by radiologists without
more » ... nerating new patient records; (2) production allows the AI-based system to generate results stored in an institution's Picture Archiving and Communication System; and (3) feedback equips radiologists with tools for editing the AI inference results for periodic retraining of the deployed AI systems, thereby allowing the continuous organic improvement of AI-based radiology-workflow solutions. A case study (i.e., detection of brain metastases with T1-weighted contrast-enhanced 3D MRI) illustrates the deployment details of a particular AI-based application according to the aforementioned maturity levels. It is shown that the given AI application significantly improves with the feedback coming from radiologists; the number of incorrectly detected brain metastases (false positives) reduces from 14.2 to 9.12 per patient with the number of subsequently annotated datasets increasing from 93 to 217 as a result of radiologist adjudication.
arXiv:1910.06424v1 fatcat:6ahoragrtza67ijof2sfvbffse