Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.
Who will be affected?
Readers: No access to all 28 journals. We recommend accessing our articles via PubMed Central
Authors: No access to the submission form or your user account.
Reviewers: No access to your user account. Please download manuscripts you are reviewing for offline reading before Wednesday, July 01, 2020 at 7:00 PM.
Editors: No access to your user account to assign reviewers or make decisions.
Copyeditors: No access to user account. Please download manuscripts you are copyediting before Wednesday, July 01, 2020 at 7:00 PM.
Current State of Community-Driven Radiological AI Deployment in Medical Imaging
Vikash Gupta;
Barbaros Selnur Erdal;
Carolina Ramirez;
Ralf Floca;
Brad Genereaux;
Sidney Bryson;
Christopher P Bridge;
Jens Kleesiek;
Felix Nensa;
Rickmer Braren;
Khaled Younis;
Tobias Penzkofer;
Andreas Michael Bucher;
Ming Melvin Qin;
Gigon Bae;
Hyeonhoon Lee;
Jorge M Cardoso;
Sebastien Ourselin;
Eric Kerfoot;
Rahul Choudhury;
Richard D White;
Tessa Cook;
David Bericat;
Matthew Lungren;
Risto Haukioja;
Haris Shuaib
ABSTRACT
Artificial intelligence (AI) has become commonplace in solving routine everyday tasks. Because of the exponential growth in medical imaging data volume and complexity, the workload on radiologists is steadily increasing. AI has been shown to improve efficiency in medical-image generation, processing, and interpretation, and various 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. The goal of this paper is to give an overview of the intersection of the AI and medical imaging landscapes. We also want to inform the readers about the importance of using standards in their radiology workflow and the challenges associated with deploying AI models in the clinical workflow. The main focus of this paper is to examine the existing condition of radiology workflow and identify the challenges hindering the implementation of AI in hospital settings. This report reflects extensive weekly discussions and practical problem-solving expertise accumulated over multiple years by industry experts, imaging informatics professionals, research scientists and clinicians. To gain a deeper understanding of the requirements for deploying AI models, we introduce a taxonomy of AI use cases, supplemented by real-world instances of AI model integration within hospitals. We will also explain how the need for AI integration in radiology can be addressed using Medical Open Network for Artificial Intelligence (MONAI). MONAI is an open-source consortium for providing reproducible deep learning solutions and integration tools for radiology practice in hospitals.
Citation
Please cite as:
Gupta V, Erdal BS, Ramirez C, Floca R, Genereaux B, Bryson S, Bridge CP, Kleesiek J, Nensa F, Braren R, Younis K, Penzkofer T, Bucher AM, Qin MM, Bae G, Lee H, Cardoso JM, Ourselin S, Kerfoot E, Choudhury R, White RD, Cook T, Bericat D, Lungren M, Haukioja R, Shuaib H
Current State of Community-Driven Radiological AI Deployment in Medical Imaging