mlsanityData to 7 Oct 2026

Levels of Autonomous Radiology

arXiv 2112.07286 ↗First posted 14 Dec 2021cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Radiology, being one of the younger disciplines of medicine with a history of just over a century, has witnessed tremendous technological advancements and has revolutionized the way we practice medicine today. In the last few decades, medical imaging modalities have generated seismic amounts of medical data. The development and adoption of Artificial Intelligence (AI) applications using this data will lead to the next phase of evolution in radiology. It will include automating laborious manual tasks such as annotations, report-generation, etc., along with the initial radiological assessment of cases to aid radiologists in their evaluation workflow. We propose a level-wise classification for the progression of automation in radiology, explaining AI assistance at each level with corresponding challenges and solutions. We hope that such discussions can help us address the challenges in a structured way and take the necessary steps to ensure the smooth adoption of new technologies in radiology.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 53 references that are in the map. First posted on arXiv 14 Dec 2021.

Builds on

11 of 53 references.

References · 11

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  3. 2020The Future of Digital Health with Federated Learningcited by 625
  4. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
  5. 2018Generative Adversarial Network in Medical Imaging: A Reviewcited by 257
  6. 2020Survey of Personalization Techniques for Federated Learningcited by 144
  7. 2020Detection of data drift and outliers affecting machine learning model performance over timecited by 19
  8. 2020Ensuring Fairness under Prior Probability Shiftscited by 16
  9. 2020Comparison of Privacy-Preserving Distributed Deep Learning Methods in Healthcarecited by 14
  10. 2021Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practicecited by 6
  11. 2020Role of Edge Device and Cloud Machine Learning in Point-of-Care Solutions Using Imaging Diagnostics for Population Screeningcited by 1

Built on by

Not cited yet.