Data Science Resources

Fellowship in Informatics

The Informatics Fellowship provides a radiology resident or early-career professional with hands-on experience in the field of informatics, including one-on-one mentoring and informatics projects. The fellow will be introduced to initiatives of the Data Science Institute® , ACR AI-LAB™ , and other ACR Informatics projects.

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Getting Started with AI

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 highlight complex ethical and societal issues. This statement highlights that the ethical use of AI in radiology should promote well-being, minimize harm and ensure that the benefits and harms are distributed among stakeholders.

Read the full statement

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA

This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.

Read the full statement

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.

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 AI functions grow.

Integrating AI in Medical Imaging

Evaluating Artificial Intelligence Systems to Guide Purchasing Decisions

Many radiologists are considering investments in AI to improve the quality of care for their patients. This article outlines considerations for the purchasing process beginning with performance evaluation.

Artificial Intelligence: A Private Practice Perspective

With scale and a focus on innovation, our practice has had the opportunity to be an early adopter of AI technology. We have gained experience identifying use cases that provide value for our patients and practice; selecting AI products and vendors; piloting vendors’ AI algorithms; creating our own AI algorithms; implementing, optimizing and maintaining these algorithms; garnering radiologist acceptance of these tools and integrating AI into our radiologists’ daily workflow.

Safe and Effective Artificial Intelligence Implementation in the Real World: A Shared Responsibility

This is a response to a letter to the editor of the Journal of the American College of Radiology (JACR) regarding the study “Real-World Performance of Large Vessel Occlusion Artificial Intelligence–Based Computer-Aided Triage and Notification Algorithms—What the Stroke Team Needs to Know.”

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.

Working With the Data

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 they be generalizable to widespread clinical practices?

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.

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, ACR has taken a leadership role in the development of data registries that support the practice of radiology.

Economic Impact

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.

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.

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 healthcare practitioners who send their patients to us for our services.

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Introduction and Keynote - Generative AI in Radiology

Computing Basics

Making AI Safe

Understandable, Auditable, and Transparent AI

The Recipe for Success In A Learning Healthcare System

The Road to Biased AI is Paved With Good Intentions

Disrupting Traditional Healthcare Models with Distributed Networks

Who should be involved in AI purchase decisions and who is responsible for monitoring AI tools?

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Find definitions for common terms related to artificial intelligence.

View the Glossary of Terms

Data Science Resources

Fellowship in Informatics

Getting Started with AI

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Integrating AI in Medical Imaging

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Working With the Data

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Economic Impact

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Find definitions for common terms related to artificial intelligence.

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Getting Started with AI

Integrating AI in Medical Imaging

Working With the Data

Economic Impact

Find definitions for common terms related to artificial intelligence.

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