Multi-institutional Evaluation and Training of Breast Density Classification AI Algorithm Using ACR Connect and AI-LAB.

Objective: To demonstrate and test the capabilities of the American College of Radiology (ACR) Connect and AI-LAB software platform by implementing multi-institutional artificial intelligence (AI) training and validation for breast density classification.

Methods: In this proof-of-concept study, six U.S.

Proceedings From the 2022 ACR-RSNA Workshop on Safety, Effectiveness, Reliability, and Transparency in AI.

Despite the surge in artificial intelligence (AI) development for health care applications, particularly for medical imaging applications, there has been limited adoption of such AI tools into clinical practice. During a 1-day workshop in November 2022, co-organized by the ACR and the RSNA, participants outlined experiences and problems with implementing AI in clinical practice, defined the needs of various stakeholders in the AI ecosystem, and elicited potential solutions and strategies related to the safety, effectiveness, reliability, and transparency of AI algorithms. Participants included radiologists from academic and community radiology practices, informatics leaders responsible for AI implementation, regulatory agency employees, and specialty society representatives.

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

Artificial intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones.

Developing, purchasing, implementing and monitoring AI tools in radiology: Practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones.

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

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones.

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

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever‑growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones.

Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones.

Projected Growth in FDA-Approved Artificial Intelligence Products Given Venture Capital Funding.

Purpose: Medical imaging accounts for 85% of digital health's venture capital funding. As funding grows, it is expected that artificial intelligence (AI) products will increase commensurately. The study's objective is to project the number of new AI products given the statistical association between historical funding and FDA-approved AI products.

Use of Artificial Intelligence in Radiology: Impact on Pediatric Patients, a White Paper From the ACR Pediatric AI Workgroup.

In this white paper, the ACR Pediatric AI Workgroup of the Commission on Informatics educates the radiology community about the health equity issue of the lack of pediatric artificial intelligence (AI), improves the understanding of relevant pediatric AI issues, and offers solutions to address the inadequacies in pediatric AI development. In short, the design, training, validation, and safe implementation of AI in children require careful and specific approaches that can be distinct from those used for adults. On the eve of widespread use of AI in imaging practice, the group invites the radiology community to align and join Image IntelliGently (www.

Keeping Patient Data Secure in the Age of Radiology Artificial Intelligence: Cybersecurity Considerations and Future Directions.

Artificial intelligence (AI)-based solutions are increasingly being incorporated into radiology workflows. Implementation of AI comes along with cybersecurity risks and challenges that practices should be aware of and mitigate for a successful and secure deployment. In this article, these cybersecurity issues are examined through the lens of the "CIA" triad framework-confidentiality, integrity, and availability.

Real-World Performance of Large Vessel Occlusion Artificial Intelligence-Based Computer-Aided Triage and Notification Algorithms-What the Stroke Team Needs to Know.

Purpose: To evaluate the real-world performance of two FDA-approved artificial intelligence (AI)-based computer-aided triage and notification (CADt) detection devices and compare them with the manufacturer-reported performance testing in the instructions for use.

Materials And Methods: Clinical performance of two FDA-cleared CADt large-vessel occlusion (LVO) devices was retrospectively evaluated at two separate stroke centers. Consecutive "code stroke" CT angiography examinations were included and assessed for patient demographics, scanner manufacturer, presence or absence of CADt result, CADt result, and LVO in the internal carotid artery (ICA), horizontal middle cerebral artery (MCA) segment (M1), Sylvian MCA segments after the bifurcation (M2), precommunicating part of cerebral artery, postcommunicating part of the cerebral artery, vertebral artery, basilar artery vessel segments.

Get With the Guidelines on MS Imaging by Leveraging Peer Learning.

Objectives: To achieve consensus on the performance, interpretation and reporting of MS imaging according to up-to-date guidelines using the Peer Learning Methodology.

Materials And Methods: We utilized the Peer Learning Methodology to engage our clinical and radiology colleagues, review the current guidelines, acheive consensus on imaging techniques and reporting standards. After implementing changes, we collected radiologist feedback on the impact of the optimized images on their interpretation.

Abdominal peer learning: advantages and lessons learned.

In 2017, our tertiary hospital-based imaging practice transitioned from score-based peer review to the peer learning methodology for learning and improvement. In our subspecialized practice, peer learning submissions are reviewed by domain experts, who then provide feedback to individual radiologists, curate cases for group learning sessions, and develop associated improvement initiatives. In this paper, we share lessons learned from our abdominal imaging peer learning submissions with the assumption that trends in our practice likely mimic others', and hope that other practices can avoid future errors and elevate the level of the quality of their own performance.

Interstitial lung abnormalities in a large clinical lung cancer screening cohort: association with mortality and ILD diagnosis.

Background: Interstitial lung abnormalities (ILA) are CT findings suggestive of interstitial lung disease in individuals without a prior diagnosis or suspicion of ILD. Previous studies have demonstrated that ILA are associated with clinically significant outcomes including mortality. The aim of this study was to determine the prevalence of ILA in a large CT lung cancer screening program and the association with clinically significant outcomes including mortality, hospitalizations, cancer and ILD diagnosis.

Artificial intelligence in oncologic imaging.

Radiology is integral to cancer care. Compared to molecular assays, imaging has its advantages. Imaging as a noninvasive tool can assess the entirety of tumor unbiased by sampling error and is routinely acquired at multiple time points in oncological practice.

Importance of tumor subtypes in cancer imaging.

Cancer therapy has evolved from being broadly directed towards tumor types, to highly specific treatment protocols that target individual molecular subtypes of tumors. With the ever-increasing data on imaging characteristics of tumor subtypes and advancements in imaging techniques, it is now often possible for radiologists to differentiate tumor subtypes on imaging. Armed with this knowledge, radiologists may be able to provide specific information that can obviate the need for invasive methods to identify tumor subtypes.

Leveraging the Peer Learning Conference to Establish and Maintain a Peer Learning Program.

Peer learning is a model of continuous feedback, learning, and improvement that is now well-recognized as a method to address radiologist errors. The peer learning conference is the most public facing cornerstone of any peer learning program, and is critical in establishing and maintaining the "Just Culture" that allows the program to thrive. We describe here our 5-step approach to organizing and moderating peer learning conferences for continued growth and participation over the past 4 years, including: achieving group buy-in, setting expectations, preparing the conference, moderating the conference, and post-conference documentation.

External COVID-19 Deep Learning Model Validation on ACR AI-LAB: It's a Brave New World.

Purpose: Deploying external artificial intelligence (AI) models locally can be logistically challenging. We aimed to use the ACR AI-LAB software platform for local testing of a chest radiograph (CXR) algorithm for COVID-19 lung disease severity assessment.

Methods: An externally developed deep learning model for COVID-19 radiographic lung disease severity assessment was loaded into the AI-LAB platform at an independent academic medical center, which was separate from the institution in which the model was trained.

Moving Toward Seamless Interinstitutional Electronic Image Transfer.

The fact that medical images are still predominately exchanged between institutions via physical media is unacceptable in the era of value-driven health care. Although better solutions are technically possible, problems of coordination and market dynamics may be inhibiting progress more than technical factors. We provide a macrosystem analysis of the problem of interinstitutional medical image exchange and propose a strategy for nudging the market toward a patient-friendly solution.

FDA-regulated AI Algorithms: Trends, Strengths, and Gaps of Validation Studies.

Rationale And Objectives: To assess key trends, strengths, and gaps in validation studies of the Food and Drug Administration (FDA)-regulated imaging-based artificial intelligence/machine learning (AI/ML) algorithms.

Materials And Methods: We audited publicly available details of regulated AI/ML algorithms in imaging from 2008 until April 2021. We reviewed 127 regulated software (118 AI/ML) to classify information related to their parent company, subspecialty, body area and specific anatomy type, imaging modality, date of FDA clearance, indications for use, target pathology (such as trauma) and findings (such as fracture), technique (CAD triage, CAD detection and/or characterization, CAD acquisition or improvement, and image processing/quantification), product performance, presence, type, strength and availability of clinical validation data.

Virtual Monoenergetic Spectral Detector CT for Preoperative CT Angiography in Liver Donors.

Objective: The purpose of this study was to evaluate the use of virtual monoenergetic images (VMI) in pre-operative CT angiography of potential donors for living donor adult liver transplantation (LDALT), and to determine the optimal energy level to maximize vascular signal-to-noise and contrast-to-noise ratios (SNR and CNR, respectively).

Materials And Methods: We retrospectively evaluated 29 CT angiography studies performed preoperatively in potential liver donors on a spectral detector CT scanner. All studies included arterial, early venous, and delayed venous phase imaging.