Ethics in Radiology: Wait Lists Queue Jumping

Education in ethics is a requirement for all Royal College residency training programs as laid out in the General Standards of Accreditation for residency programs in Canada. The ethical challenges that face radiologists in clinical practice are often different from those that face other physicians, because the nature of the physician-patient interaction is unlike that of many other specialties. Ethics education for radiologists and radiology residents will benefit from the development of teaching materials and resources that focus on the issues that are specific to the specialty. This article is intended to serve as an educational resource for radiology training programs to facilitate teaching ethics to residents and also as a continuing medical education resource for practicing radiologists. In an environment of limited health care resources, radiologists are frequently asked to expedite imaging studies for patients and, in some respects, act as gatekeepers for specialty care. The issues of wait lists, queue jumping, and balancing the needs of individuals and society are explored from the perspective of a radiologist.     

Beyond business as usual: Radiology residency educational response to the COVID-2019 pandemic

The COVID-19 pandemic has disrupted standard hospital operations and diagnostic radiology resident education at academic medical centers across the country. Deferment of elective surgeries and procedures coupled with a shift of resources toward increased inpatient clinical needs for the care of COVID-19 patients has resulted in substantially decreased imaging examinations at many institutions. Additionally, both infection control and risk mitigation measures have resulted in minimal on-site staffing of both trainees and staff radiologists at many institutions. As a result, residents have been placed in nonstandard learning environments, including working from home, engaging in a virtual curriculum, and participating in training sessions in preparation for potential reassignment to other patient care settings. Typically, for residents to gain the necessary knowledge, skills, and experience to practice independently upon graduation, radiology training programs must provide an optimal balance between resident education and clinical obligations. We describe our experience adapting to the challenges in educational interruptions and clinical work reassignments of 41 interventional and diagnostic radiology residents at a large academic center. We highlight opportunities for collaboration and teamwork in creatively adjusting and planning for the short and long-term impact of the pandemic on resident education. This experience shows how the residency educational paradigm was shifted during a pandemic and can serve as a template to address future disruptions.     

Management education during radiology residency: development of an educational practice

The practice of radiology has dramatically increased in complexity, largely due to three broad influences. These include the proliferation of imaging technologies, the economic pressures to limit healthcare costs, and the increasingly intrusive role of third parties (whether payors, regulators, or government) in everyday healthcare transactions. Practicing radiologists have been adapting to these technologic and socioeconomic changes and will continue to do so by managing the quality and scope of their professional services, the workflow of radiology operations, and the economic viability of their practices. It is likely that radiology practices would benefit from the presence of one or more radiologists with managerial training and skills. In this article, it is proposed that management education for radiologists may actually be initiated during residency; the value and the experiences with such an educational practice are described.     

Dear Medical Students - Artificial Intelligence is Not Taking Away a Radiologist's Job

Given limited exposure to radiology during the pre-clinical and clinical years, it has been challenging to recruit medical students to radiology. Now, many medical students considering radiology as a career are deterred due to misinformation surrounding how AI implementation will affect radiologists in the future. Artificial Intelligence (AI) has the potential to revolutionize the way in which medicine is practiced, especially in the field of radiology, and will ultimately support radiologists and advance the specialty. We aimed to provide a basic guide for medical students on the application of artificial intelligence in radiology, address misconceptions, highlight the role radiologists will play in AI development, and discuss the challenges faced in the future.     

Retos de la formación en radiología en la era de la inteligencia artificial

Artificial intelligence is a branch of computer science that is generating great expectations in medicine and particularly in radiology. Artificial intelligence will change not only the way we practice our profession, but also the way we teach it and learn it. Although the advent of artificial intelligence has led some to question whether it is necessary to continue training radiologists, there seems to be a consensus in the recent scientific literature that we should continue to train radiologists and that we should teach future radiologists about artificial intelligence and how to exploit it. The acquisition of competency in artificial intelligence should start in medical school, be consolidated in residency programs, and be maintained and updated during continuing medical education. This article aims to describe some of the challenges that artificial intelligencve can pose in the different stages of training in radiology, from medical school through continuing medical education.     

Factors that influence radiologists' career choices

RATIONALE AND OBJECTIVES: The authors performed this study to examine the factors, particularly the modifiable factors, that influence the career choices of radiologists immediately after graduation from residency and later. MATERIALS AND METHODS: A survey was sent to 119 radiologists who had graduated from a large academic training program between 1981 and 2000. The graduates were asked to classify their first job and any subsequent jobs in academic radiology or private practice and to identify the reasons for their initial job choices and any job changes. A nested cohort study was performed to evaluate the effect of research experience on career choice. RESULTS: Seventy-nine (66%) graduates responded to the survey. Forty-three (54%) of the respondents had chosen academic positions as their first jobs. Those who had published during their residency were 26.4 times more likely to choose an academic position as a first job. Twenty-four graduates had since left their academic jobs for private practice. Although the discrepancy in financial rewards between academic radiology and private practice was the main reason for the job switch in 71% of these cases, 33% of the respondents cited difficulty with research as a reason. In addition, only 25% of current academic radiologists were satisfied with their research activities. CONCLUSION: An exodus from academic radiology to private practice is evident among graduates from this large academic residency program, with greater financial reward being the primary motivation. However, a positive research experience during residency could persuade more graduates to choose and to continue in an academic career.     

Radiology education: a glimpse into the future

The digital revolution in radiology continues to advance rapidly. There are a number of interesting developments within radiology informatics which may have a significant impact on education and training of radiologists in the near future. These include extended functionality of handheld computers, web-based skill and knowledge assessment, standardization of radiological procedural training using simulated or virtual patients, worldwide videoconferencing via high-quality health networks such as Internet2 and global collaboration of radiological educational resources via comprehensive, multi-national databases such as the medical imaging resource centre initiative of the Radiological Society of North America. This article will explore the role of e-learning in radiology, highlight a number of useful web-based applications in this area, and explain how the current and future technological advances might best be incorporated into radiological training.     

Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement

This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. 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. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice. This statement highlights our consensus that ethical use of AI in radiology should promote well-being, minimize harm, and ensure that the benefits and harms are distributed among stakeholders in a just manner. We believe AI should respect human rights and freedoms, including dignity and privacy. It should be designed for maximum transparency and dependability. Ultimate responsibility and accountability for AI remains with its human designers and operators for the foreseeable future. The radiology community should start now to develop codes of ethics and practice for AI that promote any use that helps patients and the common good and should block use of radiology data and algorithms for financial gain without those two attributes.     

Optimizing Imaging Clinical Decision Support: Perspectives of Pediatric Emergency Department Physicians

PurposeThe aim of this study was to solicit perspectives of pediatric emergency department physicians (PEDPs) to determine how software-based clinical decision support mechanisms (CDSMs) may integrate with existing imaging clinical decision support (ICDS) to optimize imaging utilization at the authors’ institution.MethodsThrough qualitative interviews, the authors explored how PEDPs define ICDS, how they seek and obtain radiologist consultation, and how the rollout of CDSMs at the institution may potentially affect clinical practice. Codes were developed and explicitly defined through literature review and analysis of a subset of interviews. Coding results informed thematic categories used to develop an explanatory model.ResultsAnalysis revealed three major thematic categories: (1) common influences on the decision process, (2) radiology consultation experience, and (3) PEDPs’ perspectives on CDSMs. PEDPs described radiologist consultation as a valuable component of ICDS but reported difficulty in coordinating imaging strategies with radiologists and other subspecialists. PEDPs described the exchange of ideas as especially worthwhile for scenarios that do not fit neatly into clinical pathways. Barriers to radiologist consultation include time, access to radiologists, and not wanting to disrupt radiologists’ workflow. PEDPs expressed optimism that CDSMs may improve their workflow and facilitate effective interaction with radiologists.ConclusionsPEDPs suggested that radiologist consultation will continue to be a valuable component of ICDS after the implementation of CDSMs by providing discussion-driven guidance to complement CDSM recommendations. The results also indicate that radiologists may consider strategies to facilitate effective interaction with PEDPs and reconcile conflicts of CDSMs with clinical practice.     

Attitude of Brazilian dentists and dental students regarding the future role of artificial intelligence in oral radiology: a multicenter survey

Objectives:The aim of this study was to assess the attitude of dentists and dental students in Brazil regarding the impact of artificial intelligence (AI) in oral radiology, and to evaluate the effect of an introductory AI lecture on their attitude.Methods:A questionnaire was prepared, comprising statements regarding the future role of AI in oral radiology and dentistry. A lecture of approx. 1 h was prepared, comprising the basic principles of AI and a non-exhaustive overview of AI research in medicine and dentistry. Participants filled in the questionnaire prior to the lecture. After the lecture, the questionnaire was repeated.Results:Throughout 7 sessions at 6 locations, 293 questionnaires were collected. The majority of participants were undergraduate dental students (57%). Prior to the lecture, there was a strong agreement regarding the various future roles and expected impact of AI in oral radiology. Approximately, one-third of participants was concerned about AI. After the lecture, agreement regarding the different roles of AI in oral radiology increased, overall excitement regarding AI increased, and concerns regarding the potential replacement of oral radiologists decreased.Conclusions:A generally positive attitude towards AI was found; an introductory lecture was beneficial towards this attitude and alleviated concerns regarding the effect of AI on the oral radiology profession. Given the unprecedented, ongoing revolution of AI-augmented radiology, it is pivotal to incorporate AI topics in dental training curricula.     

Physician assistants in academic radiology: the harborview experience

We describe a model of how physician assistants can be used in an academic medical center to expand radiologist productivity, and to enhance the departmental academic and educational missions. At Harborview Medical Center, following a training program and graduated responsibility under supervision, physician assistants provide initial interpretation of radiology studies, consultation to referring physicians, and perform less complicated interventional procedures. Acceptance of physician assistants by the radiologists, radiology residents, and referring physicians has been high. Although the impact of physician assistants on departmental clinical productivity is difficult to measure, our data suggest that radiologists are more efficient when physician assistants are assigned to service, both in terms of numbers of studies interpreted, and timeliness of reporting and billing. As a result of the success of our program, we believe that physician assistants can have an important role in radiology practice.     

Does gender impact upon application rejection rate among Canadian radiology residency applicants?

PURPOSE: To determine if and how gender ratios have changed within Canadian radiology, and to determine if gender discrimination occurs at the level of the radiology resident selection committee. METHODS: The Canadian Medical Association, Canadian Association of Radiologists, Canadian Institute for Health Information, Royal College of Physicians and Surgeons of Canada, and Canadian Residency Matching Service provided gender-specific data. We compared the proportion of female applicants who ranked a radiology program as their top choice and were rejected from any radiology program with the corresponding proportion for male applicants. RESULTS: The numbers of women and men being awarded an MD from a Canadian university equalized nearly a decade ago. Women continue to be numerically underrepresented among practicing radiologists; however, the proportion of women continues to increase so that there is 1 female radiologist in practice to every 3 male radiologists in practice in 2005. More male medical students ranked a radiology residency training program as their top choice in the residency match; however, of those who did, they were as likely as women to be rejected from a radiology residency training program. Grouping all female and male graduating medical students participating in the residency match and ranking a radiology residency as their top choice between 1993 and 2004, the odds of men being rejected were 1.4 times (95% CI 0.99-1.9, p = 0.07) greater than for women. CONCLUSIONS: There continues to be more men than women radiologists in practice; however, the female-to-male ratio continues to increase. Our data suggest that discrimination against female applicants at the level of radiology residency selection does not occur.     

Artificial Intelligence May Cause a Significant Disruption to the Radiology Workforce

The increasingly realistic prospect of artificial intelligence (AI) playing an important role in radiology has been welcomed with a mixture of enthusiasm and anxiousness. A consensus has arisen that AI will support radiologists in the interpretation of less challenging cases, which will give the radiologists more time to focus on the challenging tasks as well as interactions with patients and other clinicians. The possibility of AI replacing a large number of radiologists is generally dismissed by the radiology community. The common arguments include the following: (1) AI will never be able to match radiologists’ performance; (2) radiologists do more than interpret images; (3) even if AI takes over a large portion of the reading tasks, the radiologists’ effort will be shifted toward interactions with patients and other physicians; (4) the FDA would never agree to let machines do the work of radiologist; (5) the issues of legal liability would be insurmountable; and (6) patients would never put complete trust in computer algorithms. In this article, I analyze these arguments in detail. I find a certain level of validity to some of them. However, I conclude that none of the arguments provide sufficient support for the claim that AI will not create a significant disruption in the radiology workforce. Such disruption is a real possibility. Although the radiology specialty has shown an astonishing ability to adapt to the changing technology, the future is uncertain, and an honest, in-depth discussion is needed to guide development of the field.     

Artificial Intelligence and Clinical Decision Support for Radiologists and Referring Providers

Recent advances in artificial intelligence (AI) are providing an opportunity to enhance existing clinical decision support (CDS) tools to improve patient safety and drive value-based imaging. We discuss the advantages and potential applications that may be realized with the synergy between AI and CDS systems. From the perspective of both radiologist and ordering provider, CDS could be significantly empowered using AI. CDS enhanced by AI could reduce friction in radiology workflows and can aid AI developers to identify relevant imaging features their tools should be seeking to extract from images. Furthermore, these systems can generate structured data to be used as input to develop machine learning algorithms, which can drive downstream care pathways. For referring providers, an AI-enabled CDS solution could enable an evolution from existing imaging-centric CDS toward decision support that takes into account a holistic patient perspective. More intelligent CDS could suggest imaging examinations in highly complex clinical scenarios, assist on the identification of appropriate imaging opportunities at the health system level, suggest appropriate individualized screening, or aid health care providers to ensure continuity of care. AI has the potential to enable the next generation of CDS, improving patient care and enhancing providers’ and radiologists’ experience.     

Dificultades en la implantación de la inteligencia artificial en la práctica radiológica: lo que el radiólogo necesita saber

Artificial Intelligence has the potential to disrupt the way clinical radiology is practiced globally. However, there are barriers that radiologists should be aware of prior to implementing Artificial Intelligence in daily practice. Barriers include regulatory compliance, ethical issues, data privacy, cybersecurity, AI training bias, and safe integration of AI into routine practice. In this article, we summarize the issues and the impact on clinical radiology.     

Comprehensive Health Care Economics Curriculum and Training in Radiology Residency

PurposeTo investigate the ability to successfully develop and institute a comprehensive health care economics skills curriculum in radiology residency training utilizing didactic lectures, case scenario exercises, and residency miniretreats.MethodsA comprehensive health care economics skills curriculum was developed to significantly expand upon the basic ACGME radiology residency milestone System-Based Practice, SBP2: Health Care Economics requirements and include additional education in business and contract negotiation, radiology sales and marketing, and governmental and private payers’ influence in the practice of radiology.ResultsA health care economics curriculum for radiology residents incorporating three phases of education was developed and implemented. Phase 1 of the curriculum constituted basic education through didactic lectures covering System-Based Practice, SBP2: Health Care Economics requirements. Phase 2 constituted further, more advanced didactic lectures on radiology sales and marketing techniques as well as government and private insurers’ role in the business of radiology. Phase 3 applied knowledge attained from the initial two phases to real-life case scenario exercises and radiology department business miniretreats with the remainder of the radiology department.ConclusionA health care economics skills curriculum in radiology residency is attainable and essential in the education of future radiology residents in the ever-changing climate of health care economics. Institution of more comprehensive programs will likely maximize the long-term success of radiology as a specialty by identifying and educating future leaders in the field of radiology.     

Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology

Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation phase in many fields, including medicine. The combination of improved availability of large datasets, increasing computing power, and advances in learning algorithms has created major performance breakthroughs in the development of AI applications. In the last 5 years, AI techniques known as deep learning have delivered rapidly improving performance in image recognition, caption generation, and speech recognition. Radiology, in particular, is a prime candidate for early adoption of these techniques. It is anticipated that the implementation of AI in radiology over the next decade will significantly improve the quality, value, and depth of radiology's contribution to patient care and population health, and will revolutionize radiologists' workflows. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI working group with the mandate to discuss and deliberate on practice, policy, and patient care issues related to the introduction and implementation of AI in imaging. This white paper provides recommendations for the CAR derived from deliberations between members of the AI working group. This white paper on AI in radiology will inform CAR members and policymakers on key terminology, educational needs of members, research and development, partnerships, potential clinical applications, implementation, structure and governance, role of radiologists, and potential impact of AI on radiology in Canada.     

Artificial Intelligence: A Private Practice Perspective

Artificial intelligence (AI) is an exciting technology that can transform the practice of radiology. However, radiology AI is still immature with limited adopters, dominated by academic institutions, and few use cases in general practice. 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. With this experience, our practice has both managed challenges and identified unexpected benefits of AI. To ensure a successful and scalable AI implementation, multiple steps are required, including preparing the data, systems, and radiologists. This article reviews our experience with AI and describes why each step is important.     

Seamless Integration of Artificial Intelligence Into the Clinical Environment: Our Experience With a Novel Pneumothorax Detection Artificial Intelligence Algorithm

Although interest in artificial intelligence (AI) has exploded in recent years and led to the development of numerous commercial and noncommercial algorithms, the process of implementing such tools into day-to-day clinical practice is rarely described in the burgeoning AI literature. In this report, we describe our experience with the successful integration of an AI-enabled mobile x-ray scanner with an FDA-approved algorithm for detecting pneumothoraces into an end-to-end solution capable of extracting, delivering, and prioritizing positive studies within our thoracic radiology clinical workflow. We also detail several sample cases from our AI algorithm and associated PACS workflow in action to highlight key insights from our experience. We hope this report can help inform other radiology enterprises seeking to evaluate and implement AI-related workflow solutions into daily clinical practice.     

Academic radiology and the emergency department: does it need changing?

RATIONAL AND OBJECTIVES: The increasing importance of imaging for both diagnosis and management in patient care has resulted in a demand for radiology services 7 days a week, 24 hours a day, especially in the emergency department (ED). We hypothesized the resident preliminary reports were better than generalist radiology interpretations, although inferior to subspecialty interpretations. MATERIALS AND METHODS: Total radiology volume through our Level I pediatric and adult academic trauma ED was obtained from the radiology information system. We conducted a literature search for error and discordant rates between radiologists of varying experience. For a 2-week prospective period, all preliminary reports generated by the residents and final interpretations were collected. Significant changes in the report were tabulated. RESULTS: The ED requested 72,886 imaging studies in 2004 (16% of the total radiology department volume). In a 2-week period, 12 of 1929 (0.6%) preliminary reports by residents were discordant to the final subspecialty dictation. In the 15 peer-reviewed publications documenting error rates in radiology, the error rate between American Board of Radiology (ABR)-certified radiologists is greater than that between residents and subspecialists in the literature and in our study. However, the perceived error rate by clinicians outside radiology is significantly higher. CONCLUSION: Sixteen percent of the volume of imaging studies comes through the ED. The residents handle off-hours cases with a radiology-detected error rate below the error rate between ABR-certified radiologists. To decrease the perceived clinician-identified error rate, we need to change how academic radiology handles ED cases.