Search

Radiology is on the verge of a technological revolution driven by artificial intelligence (including large language models), which requires robust computing and storage capabilities, often beyond the capacity of current non-cloud-based informatics systems. The cloud presents a potential solution for radiology, and we should weigh its economic and environmental implications. Recently, cloud technologies have become a cost-effective strategy by providing necessary infrastructure while reducing expenditures associated with hardware ownership, maintenance, and upgrades. Simultaneously, given the optimized energy consumption in modern cloud data centers, this transition is expected to reduce the environmental footprint of radiologic operations. The path to cloud integration comes with its own challenges, and radiology informatics leaders must consider elements such as cloud architectural choices, pricing, data security, uptime service agreements, user training and support, and broader interoperability. With the increasing importance of data-driven tools in radiology, understanding and navigating the cloud landscape will be essential for the future of radiology and its various stakeholders., (Copyright © 2023 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Generative artificial intelligence (AI) tools such as GPT-4, and the chatbot interface ChatGPT, show promise for a variety of applications in radiology and health care. However, like other AI tools, ChatGPT has limitations and potential pitfalls that must be considered before adopting it for teaching, clinical practice, and beyond. We summarize five major emerging use cases for ChatGPT and generative AI in radiology across the levels of increasing data complexity, along with pitfalls associated with each. As the use of AI in health care continues to grow, it is crucial for radiologists (and all physicians) to stay informed and ensure the safe translation of these new technologies., (Copyright © 2023 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Introduction: Concerns about radiologists being replaced by artificial intelligence (AI) from the lay media could have a negative impact on medical students' perceptions of radiology as a viable specialty. The purpose of this study was to evaluate United States of America medical students' perceptions about radiology and other medical specialties in relation to AI., Methods: An anonymous, web-based survey was sent to 32 radiology interest groups at United States medical schools. The survey was comprised of 6 questions assessing medical student perceptions of AI and its potential impact on radiology and other medical specialties. Responses were voluntary and collected over a 6-month period from November 2017 to April 2018., Results: A total of 156 students responded with representation from each year of medical school. Over 75% agreed that AI would have a significant role in the future of medicine. Most (66%) agreed that diagnostic radiology would be the specialty most greatly affected. Nearly half (44%) reported that AI made them less enthusiastic about radiology. The majority of students (57%) obtained their information about AI from online articles. Thematic analysis of free answer comments revealed mostly neutral comments towards AI, however, the negative responses were the strongest and most detailed., Conclusions: US medical students believe that AI will play a significant role in medicine, particularly in radiology. However, nearly half are less enthusiastic about the field of radiology due to AI. As the majority receive information about AI from online articles, which may have negative sentiments towards AI's impact on radiology, formal AI education and medical student outreach may help combat misinformation and help prevent the dissuading of medical students who might otherwise consider the specialty., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Purpose: Today, data surrounding most of our lives are collected and stored. Data scientists are beginning to explore applications that could harness this information and make sense of it., Materials and Methods: In this review, the topic of Big Data is explored, and applications in modern health care are considered., Results: Big Data is a concept that has evolved from the modern trend of "scientism." One of the primary goals of data scientists is to develop ways to discover new knowledge from the vast quantities of increasingly available information., Conclusions: Current and future opportunities and challenges with respect to radiology are provided with emphasis on cardiothoracic imaging.

Audience response systems have become more commonplace in radiology residency programs in the last 10 years, as a means to engage learners and promote improved learning and retention. A variety of systems are currently in use. RSNA Diagnosis Live™ provides unique features that are innovative, particularly for radiology resident education. One specific example is the ability to annotate questions with subspecialty tags, which allows resident performance to be tracked over time. In addition, deficiencies in learning can be monitored for each trainee and analytics can be provided, allowing documentation of resident performance improvement. Finally, automated feedback is given not only to the instructor, but also to the trainee. Online supplemental material is available for this article. © RSNA, 2017.

Radiology procedure codes are a fundamental part of most radiology workflows, such as ordering, scheduling, billing, and image interpretation. Nonstandardized unstructured procedure codes have typically been used in radiology departments. Such codes may be sufficient for specific purposes, but they offer limited support for interoperability. As radiology workflows and the various forms of clinical data exchange have become more sophisticated, the need for more advanced interoperability with use of standardized structured codes has increased. For example, structured codes facilitate the automated identification of relevant prior imaging studies and the collection of data for radiation dose tracking. The authors review the role of imaging procedure codes in radiology departments and across the health care enterprise. Standards for radiology procedure coding are described, and the mechanisms of structured coding systems are reviewed. In particular, the structure of the RadLex™ Playbook coding system and examples of the use of this system are described. Harmonization of the RadLex Playbook system with the Logical Observation Identifiers Names and Codes standard, which is currently in progress, also is described. The benefits and challenges of adopting standardized codes-especially the difficulties in mapping local codes to standardized codes-are reviewed. Tools and strategies for mitigating these challenges, including the use of billing codes as an intermediate step in mapping, also are reviewed. In addition, the authors describe how to use the RadLex Playbook Web service application programming interface for partial automation of code mapping. © RSNA, 2017.

The purpose of this study was to gauge patient perceptions of the RSNA Image Share Project (ISP), a pilot program that provides patients access to their imaging studies online via secure Personal Health Record (PHR) accounts. Two separate Institutional Review Board exempted surveys were distributed to patients depending on whether they decided to enroll or opt out of enrollment in the ISP. For patients that enrolled, a survey gauged baseline computer usage, perceptions of online access to images through the ISP, effect of patient access to images on patient-physician relationships, and interest in alternative use of images. The other survey documented the age and reasons for declining participation for those that opted out of enrolling in the ISP. Out of 564 patients, 470 enrolled in the ISP (83 % participation rate) and 456 of these 470 individuals completed the survey for a survey participation rate of 97 %. Patients who enrolled overwhelmingly perceived access to online images as beneficial and felt it bolstered their patient-physician relationship. Out of 564 patients, 94 declined enrollment in the ISP and all 94 individuals completed the survey for a survey participation rate of 100 %. Patients who declined to participate in the ISP cited unreliable access to Internet and existing availability of non-web-based intra-network images to their physicians. Patients who participated in the ISP found having a measure of control over their images to be beneficial and felt that patient-physician relationships could be negatively affected by challenges related to image accessibility.

Real-time mining of large research trial datasets enables development of case-based clinical decision support tools. Several applicable research datasets exist including the National Lung Screening Trial (NLST), a dataset unparalleled in size and scope for studying population-based lung cancer screening. Using these data, a clinical decision support tool was developed which matches patient demographics and lung nodule characteristics to a cohort of similar patients. The NLST dataset was converted into Structured Query Language (SQL) tables hosted on a web server, and a web-based JavaScript application was developed which performs real-time queries. JavaScript is used for both the server-side and client-side language, allowing for rapid development of a robust client interface and server-side data layer. Real-time data mining of user-specified patient cohorts achieved a rapid return of cohort cancer statistics and lung nodule distribution information. This system demonstrates the potential of individualized real-time data mining using large high-quality clinical trial datasets to drive evidence-based clinical decision-making.

Purpose: Noncompete clauses (NCs) are common in many physician employment agreements, including those of radiologists. NCs restrict radiologists' ability to perform services for anyone other than their employers, not only during the term of employment but also for a period of time after employment ends. Although courts frown on the post-termination portion as a restraint of trade, in most states, NCs will be enforced if they are deemed reasonable in duration and geography. However the practice of radiology has changed. Teleradiology is common, and improvements in telecommunications and portable devices allow radiologists to perform their services virtually anywhere. In light of these changes, are NCs still necessary for radiologists?, Methods: Eighty-six University of Maryland radiology residency alumni for whom e-mail information was available were asked to complete an online survey regarding whether they are subject to NCs, the key terms of their NCs, and their views on the continuing usefulness of NCs. A review of all state and federal cases published in the Westlaw law database in which radiologists' NCs were adjudicated was also performed., Results: Twenty-one alumni from our residency program completed the survey, representing a 24.4% response rate; 57.1% of respondents are subject to NCs. Of that group, post-termination restrictions ranged from 1 to 2 years in duration, and geographic limitations ranged from 7 to >50 miles from the employer's practice. Respondents were split as to the impact of teleradiology, with 36.8% feeling that NCs are now more necessary and 26.3% feeling that NCs are less necessary. Searches of Westlaw revealed 7 cases on point, which upheld as reasonable NCs ranging from 1 to 5 years in duration and imposing geographic limitations of 15 to 40 miles from the employer's practice., Conclusions: Although the practice of radiology has undergone significant changes, this survey shows that NCs are still widely used and are still being enforced in many courts. It is unclear whether NCs still make sense in today's practice, but it may be important to modify them to explicitly address the practice of teleradiology. NCs are common and have been upheld in court, although radiologists are split on their usefulness in this era of teleradiology. Contracts should specifically address teleradiology in NC provisions., (Copyright © 2014 American College of Radiology. All rights reserved.)

Lesion segmentation involves outlining the contour of an abnormality on an image to distinguish boundaries between normal and abnormal tissue and is essential to track malignant and benign disease in medical imaging for clinical, research, and treatment purposes. A laser optical mouse and a graphics tablet were used by radiologists to segment 12 simulated reference lesions per subject in two groups (one group comprised three lesion morphologies in two sizes, one for each input device for each device two sets of six, composed of three morphologies in two sizes each). Time for segmentation was recorded. Subjects completed an opinion survey following segmentation. Error in contour segmentation was calculated using root mean square error. Error in area of segmentation was calculated compared to the reference lesion. 11 radiologists segmented a total of 132 simulated lesions. Overall error in contour segmentation was less with the graphics tablet than with the mouse (P < 0.0001). Error in area of segmentation was not significantly different between the tablet and the mouse (P = 0.62). Time for segmentation was less with the tablet than the mouse (P = 0.011). All subjects preferred the graphics tablet for future segmentation (P = 0.011) and felt subjectively that the tablet was faster, easier, and more accurate (P = 0.0005). For purposes in which accuracy in contour of lesion segmentation is of the greater importance, the graphics tablet is superior to the mouse in accuracy with a small speed benefit. For purposes in which accuracy of area of lesion segmentation is of greater importance, the graphics tablet and mouse are equally accurate.

The current focus on the economic bottom line in health care creates the potential for radiology to become a commodity, devoid of qualitative differentiation. This trend toward commoditization has been accelerated by the globalization of imaging services (teleradiology), increased information exchange (eg, Digital Imaging and Communications in Medicine, Integrating the Healthcare Enterprise), and new technology development (eg, picture archiving and communication systems, computer-aided diagnosis). The optimum strategy for avoiding commoditization is the creation of objective quality metrics and standards throughout the medical imaging practice, which will provide a reproducible and objective means with which to differentiate imaging service deliverables on the basis of quality and clinical outcomes. These quality measures can in turn be directly tied to economic incentives (pay for performance), providing further incentive for proactive quality assurance, qualitative differentiation, and technology development centered on quality.

The digital revolution in radiology introduced the need for electronic export of medical images. However, the current export process is complicated and time consuming. In response to this continued difficulty, the Integrating the Healthcare Enterprise (IHE) initiative published the Teaching File and Clinical Trial Export (TCE) integration profile. The IHE TCE profile describes a method for using existing standards to simplify the export of key medical images for education, research, and publication. This article reviews the authors' experience in implementing the TCE profile in the following three processes: (a) the retrieval of images for a typical teaching file application within a TCE-compliant picture archiving and communication system (PACS); (b) the export of images, independent of TCE compliance of the PACS, to a typical teaching file application; and (c) the TCE-compliant transfer of images for publication. These examples demonstrate methods with which the TCE profile can be implemented to ease the burden of collecting key medical images from the PACS.

Although imaging technologies have undergone dramatic evolution over the past century, radiology reporting has remained largely static, in both content and structure. Existing free-text (prose) reports have been criticized for a number of inherent deficiencies, including inconsistencies in content, structure, organization, and nomenclature. A number of new initiatives and technologies now present the radiology community with the unique opportunity to fundamentally change the radiology report from free to structured text. These new developments include a standardized nomenclature (RadLex), automated information technologies (picture archiving and communications systems and electronic medical records), and automated data tracking and analysis software (natural-language processing). Despite the increasing availability of these tools and technologies for revolutionizing reporting, clinical, psychologic, legal, and economic challenges have collectively limited structured reporting to mammography. These challenges are most evident in the current environment of heightened expectations for improved quality, timeliness, and communication, along with increasing stress, fatigue, and malpractice concerns. In conclusion, the authors present an alternative to traditional reporting that attempts to address some of these diverse challenges while incorporating the aforementioned initiatives and technologic developments. This approach uses a graphical symbol language that is directly mapped to a standardized lexicon (RadLex) and is automatically converted into a structured hierarchical text report, which can then be much more easily searched and analyzed.

ABSTRACT Radiologists often miss nodules that may represent lung cancer on chest radiographs. We investigated whether eye movements collected during the search for lung nodules by large samples of laypeople may provide information that could assist radiologists in their detection. For that purpose, we developed a partially invisible Markov model with partially unobserved states and analyzed eye tracking data of over 100 laypeople who reviewed 14 chest X-ray images, of which seven contained a potentially cancerous nodule. We used the luminance value of the pixels in the X-ray images as prior information on the possible location of a nodule and identified six regions of interest on each image that may contain a nodule. Our study demonstrated that the eye movements recorded from laypersons contained information that may assist radiologists in the detection of nodules in chest X-rays, which has important implications for crowdsourcing of search for pulmonary nodules, which are discussed. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The need for specialized individuals to manage picture archiving and communications systems (PACS) has been recognized with the creation of a new professional title: PACS administrator. This position requires skill sets that bridge the current domains of radiology technologists (RTs), information systems analysts, and radiology administrators. Health care organizations, however, have reported difficfiulty in defining the functions that a PACS administrator should perform—a challenge compounded when the tries to combine this complex set of capabilities into one individual. As part of a larger effort to define the PACS professional, we developed an extensive but not exclusive consensus list of business, technical, and behavioral competencies desirable in the dedicated PACS professional. Through an on-line survey, radiologists, RTs, information technology specialists, corporate information officers, and radiology administrators rated the importance of these competencies. The results of this survey are presented, and the implications for implementation in training and certification efforts are discussed. [ABSTRACT FROM AUTHOR]

The authors review a decade’s experience in the nation’s first filmless radiology department and outline the challenges and rewards of the transition. They summarize their experience with 10 cautionary and informative lessons on making the process more successful, more efficient, and less stressful. A number of possible avenues of new research and assessment on the effects of filmless operation on radiologists, imaging staff, referring clinicians, and patients are highlighted. [Copyright &y& Elsevier]

There have been tremendous advances in artificial intelligence (AI) and machine learning (ML) within the past decade, especially in the application of deep learning to various challenges. These include advanced competitive games (such as Chess and Go), self-driving cars, speech recognition, and intelligent personal assistants. Rapid advances in computer vision for recognition of objects in pictures have led some individuals, including computer science experts and health care system experts in machine learning, to make predictions that ML algorithms will soon lead to the replacement of the radiologist. However, there are complex technological, regulatory, and medicolegal obstacles facing the implementation of machine learning in radiology that will definitely preclude replacement of the radiologist by these algorithms within the next two decades and beyond. While not a comprehensive review of machine learning, this article is intended to highlight specific features of machine learning which face significant technological and health care systems challenges. Rather than replacing radiologists, machine learning will provide quantitative tools that will increase the value of diagnostic imaging as a biomarker, increase image quality with decreased acquisition times, and improve workflow, communication, and patient safety. In the foreseeable future, we predict that today's generation of radiologists will be replaced not by ML algorithms, but by a new breed of data science-savvy radiologists who have embraced and harnessed the incredible potential that machine learning has to advance our ability to care for our patients. In this way, radiology will remain a viable medical specialty for years to come. [ABSTRACT FROM AUTHOR]