A new set of advanced analytics and AI tools can help mitigate the spread of COVID-19 during reopening.
By John N. Grimes, PhD | MS3, Medical Innovators Development Program, Vanderbilt University School of Medicine, Nashville, TN and Collin G. Howser, MD | IR/DR Resident, Vanderbilt University Medical Center Department of Radiology, Nashville, TN and Brent V. Savoie, MD, JD | Vice-Chair, Radiology Informatics; Section Chief, Cardiothoracic Imaging, Vanderbilt University Medical Center, Nashville, TN | May 22, 2020
A new set of advanced analytics and AI tools can help mitigate the spread of COVID-19 during reopening.
In this blog on implementing machine learning projects, we highlight the importance of planning your image-labeling process in advance and discuss considerations for software selection.
By Prasanth Prasanna, MD | Chief of Imaging Informatics, University of Utah Health Science Center, Salt Lake City, UT and Arjun Sharma, MD | Attending Radiologist, Suburban Radiologists, Oak Brook, IL | March 20, 2020
Plan your machine learning project's image-labeling process in advance and consider the project's priorities before selecting software.
While much-touted image-interpretation algorithms might one day provide autonomous care, it will be many years before they deliver the results we see with non-interpretive AI today.
By Alexander J. Towbin, MD | Chair of Radiology Informatics, and Associate Chief of Radiology, Clinical Operations and Radiology Informatics at Cincinnati Children's Hospital Medical Center, Cincinnati, OH | February 13, 2020
It will be years before image-interpretation AI delivers the results we see with non-interpretive AI today.
The DSI released 90 clinician-led use cases that are freely available to the radiology community in November 2019, bringing the number of DSI use cases to 140.
By Bibb Allen, Jr., MD, FACR | ACR DSI Chief Medical Officer | Diagnostic Radiologist, Grandview Medical Center, Birmingham, AL (with thanks to Jordan Meyer of the ACR’s Data Science Institute) | December 17, 2019
While the DSI AI use cases offer approachable examples of how AI could improve radiology practices, there is even more to them than that. Use cases are also an open invitation to those developing AI to create a new AI model — or improve on an existing one.
Only with standardized, structured report data can machine learning be trained and trusted in quality patient care. The popularity of artificial intelligence is prompting the creation of new data reporting systems that will enable algorithm development and integration into our work.
By Wende N. Gibbs, MD | Department of Radiology, Neuroradiology Division | Senior Associate Consultant, Mayo Clinic | October 14, 2019
Several societies and organizations are now building the framework and content to standardize our language and reports to improve patient care.
For radiologists to be present, fairly valued, and more patient-facing, we must embrace the new technology that is shaping a changing imaging landscape — demonstrating to our patients the true value of our expertise.
By Amy Patel, MD | Breast Radiologist, Alliance Radiology |Medical Director, Liberty Hospital Women’s Imaging | Associate Professor of Radiology, University of Missouri-Kansas City School of Medicine | September 05, 2019
For radiologists to be present, fairly valued, and more patient-facing, we must embrace AI — demonstrating to patients the value of our expertise.
An unofficial guide to what you'll learn at the Informatics Summit from those who are developing and using AI in clinical care and hospital operations.
By Christopher J. Roth, MD, MMCI | Associate Professor of Radiology, Vice Chair, Information Technology and Clinical Informatics and Director of Imaging Informatics Strategy, Duke Health | August 13, 2019
ACR's 2019 Imaging Informatics Summit brings radiology professionals, practice leaders, industry partners, and policy makers together to explore strategies for implementing AI in practice on October 5–6, 2019 in Washington, D.C.
Across the country many healthcare professionals are getting stuck on some of the more challenging AI development points — from code writing to algorithm testing and from implementation to commercialization. At this once-a-year educational and networking opportunity for the key players active in the space, those questions will be answered by those who have solved the thorny development issues.
Thought-provoking discussions about the ethical and legal issues surrounding AI in radiology.
By Carol C. Wu, MD | Associate Professor of Radiology at UT MD Anderson Cancer Center | July 30, 2019
My background as a thoracic radiologist, with a strong interest in AI, and my work on the Ethics of AI in Radiology statement has made it clear to me that there are many difficult ethics considerations in AI without a clear path to solutions. By attending the BOLD AIR Summit, I hoped to find clarity and answers through the discussions of experts in bioethics, law, and imaging from around the country — there in attendance to discuss ethical and legal issues related to the rapidly developing field of AI in radiology.
How we found an AI technology partner, brought AI to our institution and chose our first project.
By Eric P. Weinberg, MD, FACR | University of Rochester Medical College, Professor of Clinical Imaging Sciences and Medical Director of University Medical Imaging | July 03, 2019
Finding an AI technology partner for the radiology department at the University of Rochester’s Medical College was a learning process for us and a challenge to navigate successfully. Here are the steps we followed to bring AI to my institution.
Younger generations are generally willing to embrace new technology like artificial intelligence. For better or worse, AI will be shaped by them.
By Ayis Pyrros, MD | Data Science Evangelist, undergraduate medical education community | Neuroradiologist, DuPage Medical Group, Hinsdale, IL | June 11, 2019
Is artificial intelligence (AI) good, bad, or both to radiologists? As the buzz around AI has reached near-peak hype levels, many medical students are left wondering what radiology will look like as a specialty in 10 years, or if it will even exist. Will radiologists will be summarily replaced and driven to extinction by their algorithmic counterparts? It’s easy to assume that medical students have the same apprehension toward new technologies that doctors do, yet that’s far from the truth.
Don’t be misled by the headlines. Data privacy, access, and liquidity still present many challenges for healthcare AI development.
By Amy Kotsenas, MD, FACR, Council Steering Committee Liaison to the DSI | Associate Professor, Mayo Clinic, Rochester, MN | May 13, 2019
New computing technologies and the vast amounts of medical data generated now make machine learning (ML) and artificial intelligence (AI) algorithms feasible in ways that have been unachievable in the past. Lately, new AI algorithms are announced almost daily, which may lead us to conclude that access to the enormous data sets powering these innovations is no longer an issue in healthcare. Unfortunately, diverse data sets for medical algorithm training are still hard for developers to find.
The ACR Data Science Institute® ACR AI-LAB™ is a user-friendly, open, freely available, platform to enable all radiology professionals to participate in the creation, validation and use of health care artificial intelligence (AI) at their own facilities, using their own data, to meet their own clinical needs.
By Bibb Allen, Jr., MD, FACR | ACR DSI Chief Medical Officer | Diagnostic Radiologist, Grandview Medical Center, Birmingham, AL | April 04, 2019
Empowering radiologists to develop AI algorithms at their own institutions, using their own patient data, to meet their own clinical needs, could potentially accelerate the development of AI for diagnostic radiology.Currently, developments in AI for the radiological sciences are being driven by exciting research happening primarily at institutions with extensive informatics and data science resources — and primarily using single institution patient data. While some of these algorithms are achieving performance levels similar to the performance of diagnostic radiologists, it is not clear if and how these tools will be generalized to widespread clinical use across multiple institutions. Without that transition, AI will fail to have broad impact on patient care.
These challenges adversely impact our progress with AI and prevent us from moving forward.
By Jeff Rudie, MD, PhD | Fourth Year Research Track Radiology Resident and Informatics Fellow at the Hospital of the University of Pennsylvania | Incoming Neuroradiology Fellow at the University of California, San Francisco | March 18, 2019
Despite the prowess of newer deep learning methods for recognizing patterns in images and other complex forms of data, the same fundamental barriers from the past two decades still stand in the way. Here’s a take on four major barriers that are slowing the integration of AI into daily clinical radiology practice and how they are slowing radiological advances.
By setting realistic project expectations, even a small data science team can achieve measurable results with AI.
By Michael M. Moore, MD, Associate Professor of Radiology and Pediatrics, and Nabeel I. Sarwani, MD, Professor of Radiology, Penn State Health System | February 11, 2019
As a radiologist, it is difficult to escape being bombarded with discussions of AI, machine learning, big data, and how the practice of the radiology profession is going to change. Balancing the potential of AI with the current capabilities and practical considerations for implementing successful projects can be a struggle.
Radiomics may one day improve precision medicine through the clinical assessment of tumors and other diseases.
By By Raym Geis, MD | Senior Scientist, ACR Data Science Institute; Adjunct Associate Professor of Radiology, National Jewish Health, Denver, CO | December 13, 2018
Radiomics is an exciting new field of radiology. It uses AI to detect granular patterns in the pixels of medical images to predict molecular and genetic phenotypes of tumors — thus enabling precision medicine.
These first-of-their-kind use cases are building a framework to facilitate the development and implementation of AI applications that are poised to help radiology professionals in disease detection, characterization, and treatment.
By Bibb Allen, Jr., MD | ACR DSI Chief Medical Officer and Diagnostic Radiologist, Grandview Medical Center | October 29, 2018
To date, no other national medical specialty organization has developed AI use cases for developers so they can create AI algorithms that are relevant and valuable to healthcare professionals. The 50 use cases in the newly released ACR DSI TOUCH-AI Directory are building a framework to facilitate the development and implementation of AI applications that are poised to help radiology professionals in disease detection, characterization, and treatment.
As one of the handful of companies partnering with IBM's artificial intelligence project (Watson), Baptist Health navigated several challenges to maintain both data integrity and patient privacy.
By Juan Carlos Batlle, MD, MBA, | M. Bioethics, Associate Professor, FIU College of Medicine; Chief of Thoracic Imaging, Baptist Health South Florida | September 17, 2018
To be usable, massive datasets must be trimmed and categorized — largely by computer algorithms. That’s where ethical sharing of patient data comes in. Proper oversight of those data management algorithms is critical to ensure protection of sensitive patient data, especially when it comes to health systems that are sharing their information with AI and big data companies.
Radiologists will get more work done with artificial intelligence.
By By James H Thrall, MD | Physician | August 13, 2018
The fundamental question with any new imaging-related technology is whether and how it can add value — to the patient, the health system, or the radiologist. Here are three applications where AI methods can have a positive impact in different ways: 1) pre-screening of screening mammograms, 2) assessing bone age, and 3) rapid identification of life-threatening conditions.
Together, we will discover how best to use ML to help us extract more information, figure out what parts machines do well and what parts expert human radiologists do well, and make ourselves even better cyborgs.
By By Raym Geis, MD | Senior Scientist, ACR Data Science Institute; Adjunct Associate Professor of Radiology, National Jewish Health, Denver, CO | July 13, 2018
When Wilhelm Roentgen first used his machine to extract new information about what was happening inside a patient in 1895, he defined the original cyborg radiologist. This human-machine combination has evolved steadily ever since, with new machines building on the old to increase the information we obtain.
With the advent of artificial intelligence, the best radiologists will no longer be those with the sharpest eyes.
By Arun Krishnaraj, MD, MPH | Physician | June 20, 2018
Dr. Abadi enters the dimly lit reading room for the first time since her medical student rotations in radiology. After a grueling intern year in surgery, she appreciates the relative calm of the reading room and looks forward to beginning her radiology residency. As she logs into her workstation for the first time, she turns to an upper-level resident and asks, “So who’s the best radiologist in the department?”
Radiologists should learn about data science to promote effective development of AI in imaging.
By Stephen M. Borstelmann, MD | June 14, 2018
It's said that "data is the new oil." Radiologists should agree — our bread and butter is interpreting complex visual data. Enter artificial intelligence (AI), with its predictions of doom for human radiologists.
Hype aside, AI and machine learning analytics are advanced techniques used in data science, a hybrid field of computer science and statistics. These are not the statistics we remember from medical school.
Medical schools must prepare trainees for artificial intelligence-augmented practice.
By Patricia Balthazar, MD | Physician | May 17, 2018
It’s a classic medical school scenario: an attending physician on rounds in the medical ward leads a group of trainees as they stand in a circle outside of a patient’s room. One of the trainees presents the case, and then the attending fires off a series of questions for anyone in the group to answer.
To move AI algorithms into routine clinical practice, we’ll need fair compensation for their development. Figuring out how that will happen isn’t as simple as it seems.
By Bibb Allen, Jr. | Chief Medical Officer, ACR Data Science Institute | April 19, 2018
A key ingredient in moving Artificial Intelligence (AI) algorithms into routine clinical practice will be ensuring our healthcare system supports fair compensation for their development. However, figuring out how that will happen may not be as simple as it might seem.
With the so-called centaur approach, a hybrid of radiologist plus machine will define the next phase of radiology.
By Stephen M. Humphries, PhD and Raym Geis, MD | April 12, 2018
Computers are now very good at extracting meaningful information from pixel data in images. Applications of AI, such as automatic detection and recognition of faces in photos, have already become familiar in consumer products. The use of AI in medicine is taking off, and medical imaging is one of the fastest-growing application areas.
AI has crept into all aspects of our lives, and the same will be true for radiology.
By Andy DeLaO | Patient Advocate | March 20, 2018
I feel a vibration on my wrist that gently wakes me from my sleep. I roll over, touch the device on my wrist, and lay there for about five more minutes. Then I feel another vibration. I check the time on my phone. It’s 5:30 — time to get up.
My morning ritual starts in earnest: I weigh myself on my digital scale, which shows that my weight is the same as the previous day and automatically syncs with my wearable fitness tracker. As I get dressed, I ask Alexa, a voice-controlled speaker and personal assistant, to play Lose Control by Missy Elliott.
The most important activity of the ACR DSI in 2018 will be the development of detailed AI use cases for the radiological sciences.
By Bibb Allen, Jr. | Chief Medical Officer, ACR Data Science Institute | February 08, 2018
As we begin the new year, the ACR Data Science Institute (DSI) is celebrating its nine-month anniversary. While it might seem odd to count anniversaries in months, the speed of the advances in data science makes just a few months seem like years. At the 2017 RSNA meeting in November, the enthusiasm for learning more about artificial intelligence (AI) in radiological care was palpable, and the ACR DSI was well represented in the RSNA Machine Learning Showcase to share our work with RSNA attendees and industry leaders.
Radiologists must learn the language of AI to make a successful payment policy case.
By Ezequiel Silva III, MD, FACR | Physician | January 23, 2018
Artificial intelligence (AI) has its own language. So does economics. I feel pretty confident with econ-speak and can throw out phrases like intensity of work or acronyms like CPT, HOPPS, and QPP in rapid fire. But I am not as well-versed in the language of AI — a point that became apparent during a recent “Economics of AI” talk I gave to a room full of radiology AI experts.
Artificial intelligence will do more than identify findings to increase radiologists’ value.
By Woojin Kim, MD | Physician | December 14, 2017
Today, plenty of hope, fear, and hype surround the use of artificial intelligence (AI) in radiology. With media attention and many startups focused on using AI to identify findings within medical images, it’s easy for us in the radiology profession to have "tunnel vision" about AI in our field. However, it’s important to widen our aperture to see the many other ways AI can benefit medical imaging.
A list of resources curated by an IT manager provides a starting point.
By John Gagnon | IT Manager | November 03, 2017
So you want to learn about Artificial Intelligence (AI) but don’t know where to start? A Google search for “resources for artificial intelligence” yields more than 25 million results, so it’s understandable why you might feel overwhelmed. Adding “in radiology” prunes the list down to an only slightly less cumbersome 459,000 results. What follows is a brief collection of resources culled from these nearly half-million hits — a starting point to help you learn the basics of AI in medical imaging.
A patient wants radiologists to remain involved in AI era.
By David Andrews | Patient | October 04, 2017
To explain how I view artificial intelligence (AI) in medicine, I like to use car repair as an analogy. As car repair is about vehicle maintenance, medicine is, in essence, about the maintenance and repair of people.
Artificial intelligence provides another tool in the Imaging 3.0 toolkit.
By Bibb Allen Jr., MD, FACR | Physician | September 20, 2017
For the past five years, ACR’s Imaging 3.0 initiative has been the toolkit that we, as radiologists, have used to enhance our value beyond our interpretations to be better stewards of imaging appropriateness, provide a more patient-centered focus within our practices, and prepare for the evolution of healthcare financing and federal payment policy. Imaging 3.0 continues to challenge us to take ownership of our patients’ entire imaging experience, and now, we have a new tool in the kit that will help us further increase the value we bring to patient care at each step in the imaging value chain: artificial intelligence (AI).