Dipping Your Toe into the Ocean of Artificial Intelligence

Using artificial intelligence (AI) in radiology, especially in image analysis, is a rapidly growing field of research. Radiologists are going to be swimming in this unfamiliar, somewhat scary and exhilarating, ocean for the rest of their careers. Don’t just stand on the shore and watch others having all the fun. Dive right in with the help of these articles, and soon you’ll be cutting through the waters of radiological AI like a gold medalist!

Putting the AI in Radiology

When stepping onto the beach, it’s good to get the sand between your toes as you get the lay of the shoreline. Published in Radiology Today Magazine in January of 2018, ‘Putting the AI in Radiology’ gives you that sandy-toed view. It covers where radiological AI has been, what it can and cannot do for radiologists, and whether it will completely replace radiologists. 

‘”AI will augment the care we provide,” Bibb Allen, MD, FACR, CMO of the ACR’s Data Science Institute (DSI) says. “Will it, over time, change the way we practice? Probably, but most likely in a way that will be good for our professions. Early adopters will have a head start in making that transition.”’

https://www.radiologytoday.net/archive/rt0118p10.shtml accessed July 06, 2020

The Ultimate Guide to AI in Radiology

Stepping up to the water, you can gauge its temperature and temperament, but it’s hard to see what lurks below the surface. Asking folks who’ve been there before you can help.

“The ultimate guide to AI in radiology provides information on the technology, the industry, the promises, and the challenges of the AI radiology field. Currently, we are on the brink of a new era in radiology artificial intelligence. AI has had a strong focus on image analysis for a long time and has been showing promising results.”

The ultimate guide covers basic terminology, basic concepts, how AI can help the radiologist, how to get a grasp of AI in the industry, how to implement AI in daily practice, and the current challenges for AI in radiology.

https://www.quantib.com/the-ultimate-guide-to-ai-in-radiology accessed July 6, 2020

Deep Learning: A Primer for Radiologists

Getting in over your head is exhilarating for some, frightening for others. This deep dive into deep learning, published in Radio Graphics, discusses key concepts of deep learning using convolutional neural network (CNNs); describes the use of deep learning techniques in radiology for lesion classification, detection, and segmentation; and lists key technical requirements in terms of the hardware, software, and dataset required to execute deep learning.

“Deep learning is a powerful and generic artificial intelligence technique that can solve image detection, recognition, and classification tasks that previously required human intelligence. The introduction of deep learning techniques in radiology will likely assist radiologists in a variety of diagnostic tasks. Familiarity with the concepts, strengths, and limitations of computer-assisted techniques based on deep learning is critical to ensure optimal patient care.”

https://pubs.rsna.org/doi/full/10.1148/rg.2017170077 accessed July 6, 2020

Studies:

Artificial Intelligence: Is it Armageddon for Radiologists?

Published in Cureus on June 30, 2020, this recent UK study looks specifically at AI in breast radiology. It discusses artificial intelligence, the use of AI in breast imaging, points out challenges that still face AI, and examines the changing role of radiologists as AI becomes more integrated into imaging.

“Indeed, AI will change radiology, and it is not too early to incorporate it into your workplace. While there are even more grey areas that need clarification, our opinion is that AI will not replace radiologists. Still, those who incorporate AI in their daily work will probably be better off than those who don’t. Every radiologist must prepare for a future of working with machines more. We predict a future where radiologists will continue to make strides and draw many benefits from the use of more sophisticated AI systems over the next 10 years.

Chiwome L, Okojie O M, Rahman A, et al. (June 30, 2020) Artificial Intelligence: Is It Armageddon for Breast Radiologists?. Cureus 12(6): e8923. doi:10.7759/cureus.8923

https://www.cureus.com/articles/32412-artificial-intelligence-is-it-armageddon-for-breast-radiologists accessed July 6, 2020

A Survey on Deep Learning in Medical Image Analysis

Published in Medical Image Analysis in December 2017, this survey provides full immersion in the depths of AI imaging research.

“Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.”

Litjens, Geert et al. “A Survey on Deep Learning in Medical Image Analysis.” Medical Image Analysis 42 (2017): 60–88. Crossref. Web.

https://arxiv.org/abs/1702.05747 accessed July 6, 2020

Learning to swim is always a bit scary and risky, but self-confidence in the water is priceless. Knowing your way around AI, given its promise and long future with radiology, will give you the knowledge and confidence to make the most of this amazing technology. Armed with this new knowledge will you sink, or swim?