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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. The major themes that emerged fell into two categories: (1) AI product development and (2) implementation of AI-based applications in clinical practice. In particular, participants highlighted key aspects of AI product development to include clear clinical task definitions; well-curated data from diverse geographic, economic, and health care settings; standards and mechanisms to monitor model reliability; and transparency regarding model performance, both in controlled and real-world settings. For implementation, participants emphasized the need for strong institutional governance; systematic evaluation, selection, and validation methods conducted by local teams; seamless integration into the clinical workflow; performance monitoring and support by local teams; performance monitoring by external entities; and alignment of incentives through credentialing and reimbursement. Participants predicted that clinical implementation of AI in radiology will continue to be limited until the safety, effectiveness, reliability, and transparency of such tools are more fully addressed., (Copyright © 2024 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

The Radiological Society of North of America (RSNA) and the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society have led a series of joint panels and seminars focused on the present impact and future directions of artificial intelligence (AI) in radiology. These conversations have collected viewpoints from multidisciplinary experts in radiology, medical imaging, and machine learning on the current clinical penetration of AI technology in radiology and how it is impacted by trust, reproducibility, explainability, and accountability. The collective points-both practical and philosophical-define the cultural changes for radiologists and AI scientists working together and describe the challenges ahead for AI technologies to meet broad approval. This article presents the perspectives of experts from MICCAI and RSNA on the clinical, cultural, computational, and regulatory considerations-coupled with recommended reading materials-essential to adopt AI technology successfully in radiology and, more generally, in clinical practice. The report emphasizes the importance of collaboration to improve clinical deployment, highlights the need to integrate clinical and medical imaging data, and introduces strategies to ensure smooth and incentivized integration. Keywords: Adults and Pediatrics, Computer Applications-General (Informatics), Diagnosis, Prognosis © RSNA, 2024.

The Radiological Society of North America (RSNA) has held artificial intelligence competitions to tackle real-world medical imaging problems at least annually since 2017. This article examines the challenges and processes involved in organizing these competitions, with a specific emphasis on the creation and curation of high-quality datasets. The collection of diverse and representative medical imaging data involves dealing with issues of patient privacy and data security. Furthermore, ensuring quality and consistency in data, which includes expert labeling and accounting for various patient and imaging characteristics, necessitates substantial planning and resources. Overcoming these obstacles requires meticulous project management and adherence to strict timelines. The article also highlights the potential of crowdsourced annotation to progress medical imaging research. Through the RSNA competitions, an effective global engagement has been realized, resulting in innovative solutions to complex medical imaging problems, thus potentially transforming health care by enhancing diagnostic accuracy and patient outcomes. Keywords: Use of AI in Education, Artificial Intelligence © RSNA, 2024.

Fueled by new techniques, computational tools, and broader availability of imaging data, artificial intelligence has the potential to transform the practice of neuroradiology. The recent exponential increase in publications related to artificial intelligence and the central focus on artificial intelligence at recent professional and scientific radiology meetings underscores the importance. There is growing momentum behind leveraging artificial intelligence techniques to improve workflow and diagnosis and treatment and to enhance the value of quantitative imaging techniques. This article explores the reasons why neuroradiologists should care about the investments in new artificial intelligence applications, highlights current activities and the roles neuroradiologists are playing, and renders a few predictions regarding the near future of artificial intelligence in neuroradiology., (© 2020 by American Journal of Neuroradiology.)

Artificial intelligence (AI) will reshape radiology over the coming years. The radiology community has a strong history of embracing new technology for positive change, and AI is no exception. As with any new technology, rapid, successful implementation faces several challenges that will require creation and adoption of new integration technology. Use cases important to real-world application of AI are described, including clinical registries, AI research, AI product validation, and computer assistance for radiology reporting. Furthermore, the informatics technologies required for successful implementation of the use cases are described, including open Computer-Assisted Radiologist Decision Support, ACR Assist, ACR Data Science Institute use cases, common data elements (radelement.org), RadLex (radlex.org), LOINC/RSNA RadLex Playbook (loinc.org), and Radiology Report Templates (radreport.org)., (Copyright © 2019 American College of Radiology. All rights reserved.)

The American Society of Neuroradiology has teamed up with the American College of Radiology and the Radiological Society of North America to create a catalog of neuroradiology common data elements that addresses specific clinical use cases. Fundamentally, a common data element is a question, concept, measurement, or feature with a set of controlled responses. This could be a measurement, subjective assessment, or ordinal value. Common data elements can be both machine- and human-generated. Rather than redesigning neuroradiology reporting, the goal is to establish the minimum number of "essential" concepts that should be in a report to address a clinical question. As medicine shifts toward value-based service compensation methodologies, there will be an even greater need to benchmark quality care and allow peer-to-peer comparisons in all specialties. Many government programs are now focusing on these measures, the most recent being the Merit-Based Incentive Payment System and the Medicare Access Children's Health Insurance Program Reauthorization Act of 2015. Standardized or structured reporting is advocated as one method of assessing radiology report quality, and common data elements are a means for expressing these concepts. Incorporating common data elements into clinical practice fosters a number of very useful downstream processes including establishing benchmarks for quality-assurance programs, ensuring more accurate billing, improving communication to providers and patients, participating in public health initiatives, creating comparative effectiveness research, and providing classifiers for machine learning. Generalized adoption of the recommended common data elements in clinical practice will provide the means to collect and compare imaging report data from multiple institutions locally, regionally, and even nationally, to establish quality benchmarks., (© 2019 by American Journal of Neuroradiology.)

In addition to being a profession, the practice of radiology is a business, and marketing is an important part of that business. There are many facets to marketing a radiology practice. The authors present a number of ideas on how to go about doing this. Some marketing methods can be directed to both patients and referring physicians. Others should be directed just to patients, while still others should be directed just to referring physicians. Aside from marketing, many of them provide value to both target audiences., (Copyright © 2016 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

IT in health care has evolved rapidly over the past 20 years. The rise of the computer is at the core of these changes. Most agree that although these technologies have revolutionized the practice of medicine, they have additionally fostered a data revolution that is simultaneously useful and disruptive. The effective use and implementation of the right IT tools are critical to the success of the imaging profession. This article serves as a guideline to radiologists on how to build an effective IT division within an imaging enterprise from the perspective of leadership, management, and human resources. We address the process for building an IT team from the ground up and also provide recommendations for modifying an existing IT group to make it more effective. Paramount to this discussion is the concept of the imaging informatics professional and the advantage this type of training brings to a radiology department. In addition, we focus on the critical role of the physician informaticist as a liaison to bridge gaps among the IT, medical, and administrative functions in an organization., (Copyright © 2014 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

During their radiology residency, trainees must learn multiple facets of radiology practice, including the writing of radiology reports. An important factor in the trainee's development of reporting skills is feedback from the attending radiologist on the trainee's preliminary reports. The quality and quantity of feedback may vary and are not typically documented. As radiology department workloads have increased and stricter limitations have been imposed on trainee work hours, less time is available for attending radiologists and trainees to perform a joint retrospective review of radiology reports. To compensate, the authors have developed a Web-based dashboard that provides trainees with case-specific feedback about their reports. Components include an attending radiologist-trainee report discrepancy logging and communication system that is integrated with the institutional picture archiving and communication system, an automated preliminary report-final report comparator, modules showing statistics related to the discrepancy logger and report comparator components, and a Web page that unifies these components with image and report display capabilities. Both the actual report feedback and the trainee's use of the system are documented, and the resultant data may be used for evaluating trainee competence in written communication, as mandated by the Accreditation Council for Graduate Medical Education. With these tools, trainees can obtain near-real-time feedback, which may pinpoint issues that can be corrected to improve the quality of their radiology reporting. This system, although it does not supplant face-to-face training sessions with attending radiologists, can augment traditional methods of learning., (© RSNA, 2013.)

Diagnostic radiology training programs must produce highly skilled diagnostic radiologists capable of interpreting radiological examinations and communicating results to clinicians. Established training performance tools evaluate interpretive skills, but trainees' competency in reporting skills is also essential. Our semi-automated passive electronic tool entitled the Quantitative Reporting Skills Evaluation (QRSE) allows radiology training programs to evaluate the quantity of edits made to trainee preliminary reports by attending physicians as a metric to evaluate trainee reporting performance. Consecutive report pairs and metadata extracted from the radiology information system were anonymized and exported to a MySQL database. To perform the QRSE, for each report pair, open source software was first utilized to calculate the Levenshtein Percent (LP), the percent of character changes required to convert each preliminary report to its corresponding final report. The average LP (ALP), ALP for each trainee, and standard deviations were calculated. Eighty-four trainees and 56 attending radiologists interpreted 228,543 radiological examinations during the study period. The overall ALP was 6.38 %. Trainee-specific ALPs ranged from 1.1 to 15.3 %. Among trainee-specific ALPs, the standard deviation was 3.7 %. Our analysis identified five trainees with trainee-specific ALPs above 2 standard deviations from the mean and 14 trainees with trainee-specific ALPs less than 1 standard deviation below the mean. The QRSE methodology allows for the passive, quantitative, and longitudinal evaluation of the reporting skills of trainees during diagnostic radiology residency training. The QRSE identifies trainees with high and low levels of edits to their preliminary reports, as a marker for trainee overall reporting skills, and thus represents a novel performance metric for radiology training programs.

The content and prose method of radiology reporting has remained essentially unchanged for more than 100 years. By leveraging current technologies, the radiology report has the potential to be a multifunctional document providing information in a number of areas including business analytics, quality assurance and safety, regulatory reporting, research and billing. Maturation and adoption of speech recognition, the development of radiology controlled terminologies and standardized reporting templates now allow for the introduction of structured reporting into the clinical setting., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Attending radiologists routinely edit radiology trainee dictated preliminary reports as part of standard workflow models. Time constraints, high volume, and spatial separation may not always facilitate clear discussion of these changes with trainees. However, these edits can represent significant teaching moments that are lost if they are not communicated back to trainees. We created an electronic method for retrieving and displaying changes made to resident written preliminary reports by attending radiologists during the process of radiology report finalization. The Radiology Information System is queried. Preliminary and final radiology reports, as well as report metadata, are extracted and stored in a database indexed by accession number and trainee/radiologist identity. A web application presents to trainees their 100 most recent preliminary and final report pairs both side by side and in a "track changes" mode. Web utilization audits showed regular utilization by trainees. Surveyed residents stated they compared reports for educational value, to improve future reports, and to improve patient care. Residents stated that they compared reports more frequently after deployment of this software solution and that regular assessment of their work using the Report Comparator allowed them to routinely improve future report quality and improved radiological understanding. In an era with increasing workload demands, trainee work hour restrictions, and decentralization of department resources (e.g., faculty, PACS), this solution helps to retain an important part of the educational experience that would have otherwise run the risk of being lost and provides it to the trainees in an efficient and highly consumable manner.

Computer-based learning, or "e-learning," plays an increasingly important role in healthcare education. Modular, standardized approaches to e-learning make learning content portable, reusable, and interoperable. The Shareable Content Object Reference Model (SCORM) is a standard for educational content that builds upon existing standards for e-learning. The "SCORM for Healthcare" model, which is designed to meet the particular needs of learning in health professions, is being developed to improve communication of educational needs, goals, and competencies among healthcare workers, educational providers, and certifying organizations. Radiologists can benefit from SCORM by becoming familiar with its features and the opportunities it affords to improve online learning.

There has been widespread adoption of personal digital assistants (PDAs) within medicine in recent years. However, information on the prevalence and usage of these devices among radiologists is limited. A survey was designed and mailed to randomly selected members of the Radiological Society of North America to determine the percentage of PDA users, their use patterns, and the types of applications that they would like to see in the future. The use patterns of attending radiologists were compared with those of trainees (residents and fellows). Overall usage was also compared with the relevant findings in two surveys of internal medicine users. It was found that slightly less than one-half of respondents used PDAs on a daily basis, a finding that was comparable to that in the internal medicine surveys. However, less than one-quarter of PDA users had radiology-specific applications installed on their devices, whereas a greater percentage of internal medicine users had software such as drug databases and clinical references on their PDAs. Radiology trainees had a higher rate of both PDA ownership and radiology application usage than did attending radiologists. It is likely that, as PDA hardware becomes more powerful, with higher display resolution, better wireless networking capabilities, and greater memory capacity, PDA ownership as well as radiology application usage will increase., ((c) RSNA, 2005.)

The next phase of the digital revolution in medicine is taking place through the dissemination of powerful handheld computers. Handheld computers, or personal digital assistants (PDAs), are no longer considered either a curiosity or a toy. The current handheld computer has many features (Internet access, simple e-mail client software, spreadsheet and database programs, word processing, and digital media) that make it an ideal tool for healthcare providers. Improvements in handwriting recognition, display characteristics, and wireless networking capabilities provide a platform for real-time review of both large static and dynamic repositories of patient data. Although earlier PDA models lacked the ability to display medical images appropriately, current PDAs boast display characteristics that approach low-resolution computer monitors. Although the handheld computer is not yet a reliable option for soft-copy reading, it offers many features that can improve work flow and efficiency for the radiologist. These features include improved personal information management, decision support via access to educational materials, and remote access to radiology-related information systems., (Copyright RSNA, 2003)

Purpose: To determine the level of participation by nonradiologists in performing neuroradiologic examinations., Materials and Methods: Medicare part B claims data from fiscal year 1992 were analyzed for CPT (current procedural terminology) codes related to computed tomography (CT) and magnetic resonance (MR) imaging of the brain, head and neck, and spine, as well as myelography, angiography, and diskography. Data were tabulated by place of service (hospital-based vs freestanding imaging centers) and by medical specialty., Results: Among 363,224 Medicare claims for CT and MR imaging of the brain, head and neck, and spine, 91% of the examinations were performed in hospitals and 9% in offices or freestanding centers; 98% of studies were interpreted by a radiologist. The largest share of radiology billing by nonradiologists was from office-based or freestanding imaging centers (9%), versus 2% at hospital-based facilities., Conclusion: Radiologists perform the vast majority of neuroradiologic examinations. Most neuroradiologic examinations performed by nonradiologists are from neurologists at freestanding/office-based imaging centers.

Fueled by new techniques, computational tools, and broader availability of imaging data, artificial intelligence has the potential to transform the practice of neuroradiology. The recent exponential increase in publications related to artificial intelligence and the central focus on artificial intelligence at recent professional and scientific radiology meetings underscores the importance. There is growing momentum behind leveraging artificial intelligence techniques to improve workflow and diagnosis and treatment and to enhance the value of quantitative imaging techniques. This article explores the reasons why neuroradiologists should care about the investments in new artificial intelligence applications, highlights current activities and the roles neuroradiologists are playing, and renders a few predictions regarding the near future of artificial intelligence in neuroradiology.