Current State of Community-Driven Radiological AI Deployment in Medical Imaging

by   Vikash Gupta, et al.

Artificial Intelligence (AI) has become commonplace to solve routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload on radiologists is steadily increasing. We project that the gap between the number of imaging exams and the number of expert radiologist readers required to cover this increase will continue to expand, consequently introducing a demand for AI-based tools that improve the efficiency with which radiologists can comfortably interpret these exams. AI has been shown to improve efficiency in medical-image generation, processing, and interpretation, and a variety of such AI models have been developed across research labs worldwide. However, very few of these, if any, find their way into routine clinical use, a discrepancy that reflects the divide between AI research and successful AI translation. To address the barrier to clinical deployment, we have formed MONAI Consortium, an open-source community which is building standards for AI deployment in healthcare institutions, and developing tools and infrastructure to facilitate their implementation. This report represents several years of weekly discussions and hands-on problem solving experience by groups of industry experts and clinicians in the MONAI Consortium. We identify barriers between AI-model development in research labs and subsequent clinical deployment and propose solutions. Our report provides guidance on processes which take an imaging AI model from development to clinical implementation in a healthcare institution. We discuss various AI integration points in a clinical Radiology workflow. We also present a taxonomy of Radiology AI use-cases. Through this report, we intend to educate the stakeholders in healthcare and AI (AI researchers, radiologists, imaging informaticists, and regulators) about cross-disciplinary challenges and possible solutions.


page 13

page 15


Bridging the gap between AI and Healthcare sides: towards developing clinically relevant AI-powered diagnosis systems

This work aims to identify/bridge the gap between Artificial Intelligenc...

CheXstray: Real-time Multi-Modal Data Concordance for Drift Detection in Medical Imaging AI

Rapidly expanding Clinical AI applications worldwide have the potential ...

PANDA: Facilitating Usable AI Development

Recent advances in artificial intelligence (AI) and machine learning hav...

OPTIMAM Mammography Image Database: a large scale resource of mammography images and clinical data

A major barrier to medical imaging research and in particular the develo...

Securing Biomedical Images from Unauthorized Training with Anti-Learning Perturbation

The volume of open-source biomedical data has been essential to the deve...

Deployment of Image Analysis Algorithms under Prevalence Shifts

Domain gaps are among the most relevant roadblocks in the clinical trans...

Please sign up or login with your details

Forgot password? Click here to reset