White Paper: Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement
AI has great potential to increase efficiency and accuracy throughout radiology, but it also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence and highlights complex ethical and societal issues. This statement highlights that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders. View the full statement.
White Paper: AI-Lab Reference Architecture Framework
For a healthcare institution to include artificial intelligence (AI) workflows in medical imaging, an architecture must be designed and implemented to support it. This architecture supports the workflows of inference, model validation, model training, and study annotation, and scales according to the needs of the institution.
White Paper: Artificial Intelligence and the Practice of Radiology: An Alternative View
The advent of computers that can accurately interpret diagnostic imaging studies will upend the practice of radiology. The two currently unanswered questions are just how much upending there will be and how long it will take to happen.
White Paper: The Radiologist’s Imitation Game
The recently released films The Imitation Game, Her, and Ex Machina have brought a philosophical quandary to the forefront of popular culture: can machines think or, at least, display behavior indistinguishable from that of a thinking human?
A Primer on Machine Learning
Machine Learning (ML) and other artificial intelligence (AI) tools have become a staple in the non-medical and medical news as these techniques are applied to increasingly complex challenges.
ML 101: The Radiologist's Basic Guide
From IBM's Watson to CAD, most radiologists have heard of machine learning. But do you know how this technique is already used in the field? Plus, what does the future hold? The ACR Bulletin brings you FAQs so you can be sure to have the basics down pat.
Exploring the Unknown
New innovations are poised to revolutionize radiology. What will these changes mean for your patients?
Machine Learning and Deep Learning, Big Data, and Science in Radiology
Is machine learning as bad for radiology as they say?
Sizing Up Technology Symbiosis
Specialists who embrace these new developments have a bigger toolbox than ever.
Riding the Technology Wave
Decision support for radiologists rises to the point of care.
Evaluating the impending impacts of the machine-learning economy
Rest Assured, João, You are Safe From Artificial Intelligence
Dr. Siegal responds to a first-year radiology resident concerned for the future of radiologists as the functions of artificial intelligence grow.
White Paper: The Artificial Intelligence Ecosystem for the Radiological Sciences: Ideas to Clinical Practice
For the most part, individual AI software developers are currently working with individual radiologists at single institutions to create AI algorithms. These developers are using a single institution’s prior imaging data for training and testing the algorithms, and the algorithm output is specifically tailored to that site’s perspective of the clinical workflow. Will theybe generalizable to widespread clinical practices?
White Paper: Big Data Management, Access, and Protection
It’s hard for a day to go by without some reminder of the explosion of big data, artificial intelligence, machine learning, and data science. Although sometimes used interchangeably, these terms refer to the ability to leverage massive amounts of data to produce algorithms that enhance or substitute for human cognition or executive tasks prone to disruption by human foibles.
White Paper: ACR Registries Serve Multiple Purposes
One of the singular advantages of working in the digital age is the ability to collect data that can be aggregated and shared for multiple purposes, including improvement of the quality of care offered to our patients and improvement of our management abilities. To help accomplish these goals, the ACR has taken a leadership role in the development of data registries that support the practice of radiology.
ACR Select® helps providers determine the most appropriate imaging exams at the point of care.
Striking a Balance
The ACR Dose Index Registry helps hospitals retain scan quality while successfully lowering radiation dose.
Sharpening the Tools
Advocate Lutheran General Hospital deploys cutting-edge technologies to manage contrast and radiation dose to enhance patient safety.
A “Big Data” Registry
The ACR Dose Index Registry is helping radiologists compare CT dose indices to national standards and safeguard patients.
The Real-Time Benefits of Registries
Registry participation is essential for quality improvement and, now under MACRA, reimbursement.
Structured for Care
An academic tertiary care center implements structured reporting, achieving 94 percent compliance among radiologists.
Structured for Success
One North Carolina practice establishes a business case for structured reports.
Setting the Standard
In today's value-driven health care environment, standardized language in structured reports allows for improved patient care.
The Perfect Radiology Report
For the greatest impact on patient care, radiologists must write clear and concise reports.
White Paper: Artificial Intelligence Using Open Source BI-RADS Data Exemplifying Potential Future Use
AI and radiologists working together can achieve better results, helping in case-based decision making. Continued evaluation of the metrics involved in predictor handling by AI algorithms will provide new insights into imaging.
White Paper: What Health Care Can Learn From Self-Driving Vehicles
In radiology, having AI perform the mundane tasks that humans may struggle with or find interminable, such as pulmonary nodule detection or organ segmentation, as well as the tasks that are simply impossible for humans, such as extracting lesion textural features across multiple contrast phases or sequences, frees us to interact with the images in ways that can push the boundaries of diagnostic science.
White Paper: Creation of an Open Framework for Point-of-Care Computer-Assisted Reporting and Decision Support Tools for Radiologists
Decreasing unnecessary variation in radiology reporting and producing guideline-concordant reports is fundamental to radiology’s success in value-based payment models and good for patient care. In this article, we present an open authoring system for point-of-care clinical decision support tools integrated into the radiologist reporting environment referred to as the computer-assisted reporting and decision support (CAR/DS) framework.
Translating What’s Relevant to Radiologists
Radiologists are working with DSI to establish use case to help prioritize development so that AI tools can help provide the highest value for patients.
An Extra Set of Eyes
Radiologists in Virginia are trialing a beta version of an application that uses artificial intelligence to detect findings on CT exams.
White Paper: Artificial Intelligence and Radiology: Collaboration Is Key
If we can work together with other entities to facilitate the transition of AI from theory to practical application in radiology, benefits will abound for all involved, with a more rapid introduction of AI to radiology in ways that are best suited to aid radiologists (as opposed to replacing them), ultimately allowing for improved care for patients.
White Paper: IT: From Complement to Substitute
Technologies that improve worker productivity are key drivers of economic growth. A handful of powerful general-purpose technologies have greatly accelerated the normal rate of growth.
White Paper: Adding Value Isn’t an Option Anymore
As practicing radiologists, we believe the benefits of our services are self-evident. However, there is a relative paucity of literature that clearly documents our value from the perspective of the health care practitioners who send their patients to us for our services.
The Economics of Machine Learning
How will emerging technology affect radiology in the near future?
Will Watson Replace Radiologists
Do Deep-Learning Machines Still Need Human Radiologists?
Presentations by Keith Dreyer, DO, PhD, FACR, FSIIM, chief data science and information officer in the radiology departments of Massachusetts General Hospital and Brigham and Women’s Hospital
In the second part of our conversation on AI, machine learning, and computer-aided diagnosis, we are joined by David Louis, MD, pathologist-in-chief at Massachusetts General Hospital. In this episode, Louis discusses how pathology has approached computer-aided diagnosis, how pathologists are confronting the fears and challenges of potential AI implementations, and his outlook for an exciting future in diagnostic medicine.
Mark Michalski, MD, executive director of the Center for Clinical Data Science at Massachusetts General and Brigham Hospital, joins us as we discuss the history of AI, how it's being used today, how it might change radiology, and how radiologists can leverage this new technology to provide even more value and better patient care.