Training the New Radiologists: Approaches for Education.

The field of Radiology is continually changing, requiring corresponding evolution in both medical student and resident training to adequately prepare the next generation of radiologists. With advancements in adult education theory and a deeper understanding of perception in imaging interpretation, expert educators are reshaping the training landscape by introducing innovative teaching methods to align with increased workload demands and emerging technologies. These include the use of peer and interdisciplinary teaching, gamification, case repositories, flipped-classroom models, social media, and drawing and comics. This publication aims to investigate these novel approaches and offer persuasive evidence supporting their incorporation into the updated Radiology curriculum.

Educating the next generation of radiologists: a comparative report of ChatGPT and e-learning resources

Rapid technological advances have transformed medical education, particularly in radiology, which depends on advanced imaging and visual data. Traditional electronic learning (e-learning) platforms have long served as a cornerstone in radiology education, offering rich visual content, interactive sessions, and peer-reviewed materials. They excel in teaching intricate concepts and techniques that necessitate visual aids, such as image interpretation and procedural demonstrations. However, Chat Generative Pre-Trained Transformer (ChatGPT), an artificial intelligence (AI)-powered language model, has made its mark in radiology education. It can generate learning assessments, create lesson plans, act as a round-the-clock virtual tutor, enhance critical thinking, translate materials for broader accessibility, summarize vast amounts of information, and provide real-time feedback for any subject, including radiology. Concerns have arisen regarding ChatGPT's data accuracy, currency, and potential biases, especially in specialized fields such as radiology. However, the quality, accessibility, and currency of e-learning content can also be imperfect. To enhance the educational journey for radiology residents, the integration of ChatGPT with expert-curated e-learning resources is imperative for ensuring accuracy and reliability and addressing ethical concerns. While AI is unlikely to entirely supplant traditional radiology study methods, the synergistic combination of AI with traditional e-learning can create a holistic educational experience.

Radiologists' perceptions of knowledge and training required by radiographers in the interpretation of radiographic images: An explorative study in KwaZulu-Natal province, South Africa.

INTRODUCTION: The health sector of South Africa is burdened by the shortage of radiologists leading to the under-reporting of radiographic images and ultimately mismanagement of patients. Previous studies have recommended training of radiographers in radiographic image interpretation in order to improve the reporting. There is paucity of information regarding the knowledge and training required by radiographers to interpret radiographic images. The purpose of this study was therefore to explore the knowledge and training required by diagnostic radiographers, according to radiologists, for the interpretation of radiographs. METHOD: A qualitative descriptive study employing criterion sampling to select qualified radiologists practicing in the eThekwini district of the KwaZulu Natal province, was conducted. One-on-one and in-depth, semi-structured interviews were used to collect data from three participants. The interviews were not carried out face to face as a result of the Covid 19 pandemic and the regulation of social distancing. This did not permit engagement with research communities. The data from the interviews were analysed using Tesch's eight steps for analysing qualitative data. RESULTS: Findings revealed that radiologists supported the interpretation of radiographic images by radiographers in rural settings, and for the radiographer's scope of practice to be restructured to include the reporting of chest and the musculoskeletal system images. The themes that emerged from the analysis included knowledge, training, clinical competencies and medico-legal responsibilities required by radiographers in the interpretation of radiographic images. CONCLUSION: Although the radiologists support the training of radiographers in the interpretation of radiographic images, radiologists think that the scope of practice should be limited to the interpretation of the chest and musculoskeletal systems in rural areas only.

Insights from nursing for radiologists.

We have observed that former nurses often make very good radiology residents, which leads us to think that nursing offers important lessons to radiology. To be clear, we are not proposing that undergraduate or medical students pursue nursing training so they can enhance their performance in residency - in view of the long course of radiology training, such a suggestion would be highly impractical. But we do believe that aspects of nursing training and practice not typically emphasized in medical education can help radiologists perform better and ultimately promote better patient care.

Ablación por radiofrecuencia en la enfermedad tiroidea y paratiroidea / Radiofrequency ablation for thyroid and parathyroid disease

La ablación por radiofrecuencia (ARF) es un método bien conocido, seguro y eficaz para tratar los nódulos tiroideos benignos, los cánceres tiroideos recurrentes, así como los adenomas de paratiroides, con resultados prometedores en los últimos años. Los dispositivos empleados y las técnicas básicas para la ARF fueron introducidos por la Sociedad Coreana de Radiología de Tiroides (KSThR) en 2012, si bien la ARF se ha aprobado en todo el mundo, con avances posteriores tanto en dispositivos como en técnica.El objetivo de esta revisión es instruir a los radiólogos intervencionistas que pretendan realizar, o que ya estén realizando, intervenciones de ARF, así como especialistas en tiroides y paratiroides que brinden atención pre y postoperatoria, acerca de la capacitación, la ejecución y el control de calidad de la ARF de los nódulos tiroideos y adenomas paratiroideos, para optimizar la eficacia del tratamiento y la seguridad del paciente.(AU)

Radiofrequency ablation is a well-known, safe, and effective method for treating benign thyroid nodules and recurring thyroid cancer as well as parathyroid adenomas that has yielded promising results in recent years. Since the Korean Society of Thyroid Radiology introduced the devices and the basic techniques for radiofrequency ablation in 2012, radiofrequency ablation has been approved all over the world and both the devices and techniques have improved.This review aims to instruct interventional radiologists who are doing or intend to start doing radiofrequency ablation of thyroid and parathyroid lesions, as well as thyroid and parathyroid specialists who provide pre- and post-operative care, in the training, execution, and quality control for radiofrequency ablation of thyroid nodules and parathyroid adenomas to optimize the efficacy and safety of the treatment.(AU)

Creation of a protective space between the rectum and prostate prior to prostate radiotherapy using a hydrogel spacer.

The placement of a polyethylene glycol (PEG) hydrogel spacer is a recently developed technique employed to reduce the radiation dose administered to the rectum during prostate radiotherapy. This procedure has been adopted by urologists and radiation oncologists involved in transperineal prostate biopsy and brachytherapy, and more recently by radiologists with experience in transperineal prostate procedures. Radiologists should be familiar with the product, which may be encountered on computed tomography (CT) or magnetic resonance imaging (MRI). Radiologists may wish to become involved in the delivery of this increasingly utilised procedure. This review familiarises radiologists with the technique and risks and benefits of the use of transperineal delivery of hydrogel spacers with imaging examples.

Retos de la formación en radiología en la era de la inteligencia artificial / Challenges of Radiology education in the era of artificial intelligence

La inteligencia artificial (IA) es una rama de las ciencias computacionales que está generando enormes expectativas en la medicina en general y en la radiología en particular. La IA no va a alterar solo la forma en que ejercemos la radiología, sino que también va a impactar en el modo en que la enseñamos y la aprendemos. Aunque se ha llegado a cuestionar la necesidad de seguir formando radiólogos como consecuencia de la llegada de la IA, la literatura científica reciente parece estar de acuerdo en que debemos seguir formándolos, incorporando a su capacitación nuevos conocimientos y competencias en IA. Esta nueva formación debería comenzar en la fase universitaria, consolidarse durante la residencia y mantenerse durante la etapa de formación continuada. Este artículo pretende describir algunos de los desafíos que la IA puede plantear en las diferentes fases formativas del radiólogo, desde la educación universitaria hasta la formación continuada.(AU)

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.(AU)

Interobserver variability in the evaluation of primary graft dysfunction after lung transplantation: impact of radiological training and analysis of discordant cases.

PURPOSE: Radiologic criteria for the diagnosis of primary graft dysfunction (PGD) after lung transplantation are nonspecific and can lead to misinterpretation. The primary aim of our study was to assess the interobserver agreement in the evaluation of chest X-rays (CXRs) for PGD diagnosis and to establish whether a specific training could have an impact on concordance rates. Secondary aim was to analyze causes of interobserver discordances. MATERIAL AND METHODS: We retrospectively enrolled 164 patients who received bilateral lung transplantation at our institution, between February 2013 and December 2019. Three radiologists independently reviewed postoperative CXRs and classified them as suggestive or not for PGD. Two of the Raters performed a specific training before the beginning of the study. A senior thoracic radiologist subsequently analyzed all discordant cases among the Raters with the best agreement. Statistical analysis to calculate interobserver variability was percent agreement, Cohen's kappa and intraclass correlation coefficient. RESULTS: A total of 473 CXRs were evaluated. A very high concordance among the two trained Raters, 1 and 2, was found (K = 0.90, ICC = 0.90), while a poorer agreement was found in the other two pairings (Raters 1 and 3: K = 0.34, ICC = 0.40; Raters 2 and 3: K = 0.35, ICC = 0.40). The main cause of disagreement (52.4% of discordant cases) between Raters 1 and 2 was the overestimation of peribronchial thickening in the absence of unequivocal bilateral lung opacities or the incorrect assessment of unilateral alterations. CONCLUSION: To properly identify PGD, it is recommended for radiologists to receive an adequate specific training.

A machine and human reader study on AI diagnosis model safety under attacks of adversarial images.

While active efforts are advancing medical artificial intelligence (AI) model development and clinical translation, safety issues of the AI models emerge, but little research has been done. We perform a study to investigate the behaviors of an AI diagnosis model under adversarial images generated by Generative Adversarial Network (GAN) models and to evaluate the effects on human experts when visually identifying potential adversarial images. Our GAN model makes intentional modifications to the diagnosis-sensitive contents of mammogram images in deep learning-based computer-aided diagnosis (CAD) of breast cancer. In our experiments the adversarial samples fool the AI-CAD model to output a wrong diagnosis on 69.1% of the cases that are initially correctly classified by the AI-CAD model. Five breast imaging radiologists visually identify 29%-71% of the adversarial samples. Our study suggests an imperative need for continuing research on medical AI model's safety issues and for developing potential defensive solutions against adversarial attacks.

Holistic processing only? The role of the right fusiform face area in radiological expertise.

Radiologists can visually detect abnormalities on radiographs within 2s, a process that resembles holistic visual processing of faces. Interestingly, there is empirical evidence using functional magnetic resonance imaging (fMRI) for the involvement of the right fusiform face area (FFA) in visual-expertise tasks such as radiological image interpretation. The speed by which stimuli (e.g., faces, abnormalities) are recognized is an important characteristic of holistic processing. However, evidence for the involvement of the right FFA in holistic processing in radiology comes mostly from short or artificial tasks in which the quick, 'holistic' mode of diagnostic processing is not contrasted with the slower 'search-to-find' mode. In our fMRI study, we hypothesized that the right FFA responds selectively to the 'holistic' mode of diagnostic processing and less so to the 'search-to-find' mode. Eleven laypeople and 17 radiologists in training diagnosed 66 radiographs in 2s each (holistic mode) and subsequently checked their diagnosis in an extended (10-s) period (search-to-find mode). During data analysis, we first identified individual regions of interest (ROIs) for the right FFA using a localizer task. Then we employed ROI-based ANOVAs and obtained tentative support for the hypothesis that the right FFA shows more activation for radiologists in training versus laypeople, in particular in the holistic mode (i.e., during 2s trials), and less so in the search-to-find mode (i.e., during 10-s trials). No significant correlation was found between diagnostic performance (diagnostic accuracy) and brain-activation level within the right FFA for both, short-presentation and long-presentation diagnostic trials. Our results provide tentative evidence from a diagnostic-reasoning task that the FFA supports the holistic processing of visual stimuli in participants' expertise domain.

Medical app minefield: radiologists use of medical apps for education and reporting and do they require regulation?

AIM: To evaluate the use of apps in radiology and consider advised changes to practice. MATERIALS AND METHODS: A survey was conducted of all radiology consultants and specialty trainees within Devon and Cornwall. The responses were collated, including the list of all medical applications used. These were assessed using the Medicine & Healthcare Products Regulatory Agency (MHRA) "Medical device stand-alone software including apps" guidance. RESULTS: The response rate was 88/150 (59%) radiologists who responded with the majority 48/88 (54.4%) using apps. Forty-four of 66 (67%) states that they did not assess the reliability or accuracy of these devices prior to use with 71/81 (88%) indicating that they were unaware of any regulations. Thirty-three items were identified of which 27 functioning apps were identified and three of these were considered medical devices and did not have complete and recognisable CE marking as required by the MHRA. CONCLUSION: This study highlights that application use is widespread. The vast majority of these applications are not considered medical devices; however, there are some devices that, according to the MHRA flow chart, are used in a way that classifies them as medical devices and should therefore be CE marked. This highlights the need for guidance and regulation of the medical application market with recommendations provided.

Establishing a Women-in-Radiology Group: A Toolkit From the American Association for Women in Radiology.

Despite increasing representation in medical schools and surgical specialties, recruitment of women into radiology has failed to exhibit commensurate growth. Furthermore, women are less likely than men to advance to leadership roles in radiology. A women-in-radiology (WIR) group provides a robust support system that has been shown to produce numerous benefits to the group's individual participants as well as the group's institution or practice. These benefits include development of mentor-ship relationships, guidance of career trajectories, improved camaraderie, increased participation in scholarly projects, and increased awareness of gender-specific issues. This article describes a recommended pathway to establishing a WIR group, with the goal of fostering sponsorship and promoting leadership, recruitment, and advancement of women in radiology. We consider barriers to implementation and review resources to facilitate success, including a range of resources provided by the American Association for Women in Radiology. By implementing the provided framework, radiologists at any career stage can start a WIR group, to promote the advancement of their female colleagues.

Performance and educational training of radiographers in lung nodule or mass detection: Retrospective comparison with different deep learning algorithms.

ABSTRACT: The aim of this investigation was to compare the diagnostic performance of radiographers and deep learning algorithms in pulmonary nodule/mass detection on chest radiograph.A test set of 100 chest radiographs containing 53 cases with no pathology (normal) and 47 abnormal cases (pulmonary nodules/masses) independently interpreted by 6 trained radiographers and deep learning algorithems in a random order. The diagnostic performances of both deep learning algorithms and trained radiographers for pulmonary nodules/masses detection were compared.QUIBIM Chest X-ray Classifier, a deep learning through mass algorithm that performs superiorly to practicing radiographers in the detection of pulmonary nodules/masses (AUCMass: 0.916 vs AUCTrained radiographer: 0.778, P < .001). In addition, heat-map algorithm could automatically detect and localize pulmonary nodules/masses in chest radiographs with high specificity.In conclusion, the deep-learning based computer-aided diagnosis system through 4 algorithms could potentially assist trained radiographers by increasing the confidence and access to chest radiograph interpretation in the age of digital age with the growing demand of medical imaging usage and radiologist burnout.

Improving radiologist's ability in identifying particular abnormal lesions on mammograms through training test set with immediate feedback.

It has been shown that there are differences in diagnostic accuracy of cancer detection on mammograms, from below 50% in developing countries to over 80% in developed world. One previous study reported that radiologists from a population in Asia displayed a low mammographic cancer detection of 48% compared with over 80% in developed countries, and more importantly, that most lesions missed by these radiologists were spiculated masses or stellate lesions. The aim of this study was to explore the performance of radiologists after undertaking a training test set which had been designed to improve the capability in detecting a specific type of cancers on mammograms. Twenty-five radiologists read two sets of 60 mammograms in a standardized mammogram reading room. The first test set focused on stellate or spiculated masses. When radiologists completed the first set, the system displayed immediate feedback to the readers comparing their performances in each case with the truth of cancer cases and cancer types so that the readers could identify individual-based errors. Later radiologists were asked to read the second set of mammograms which contained different types of cancers including stellate/spiculated masses, asymmetric density, calcification, discrete mass and architectural distortion. Case sensitivity, lesion sensitivity, specificity, receiver operating characteristics (ROC) and Jackknife alternative free-response receiver operating characteristics (JAFROC) were calculated for each participant and their diagnostic accuracy was compared between two sessions. Results showed significant improvement among radiologists in case sensitivity (+ 11.4%; P < 0.05), lesion sensitivity (+ 18.7%; P < 0.01) and JAFROC (+ 11%; P < 0.01) in the second set compared with the first set. The increase in diagnostic accuracy was also recorded in the detection of stellate/spiculated mass (+ 20.6%; P < 0.05). This indicated that the performance of radiologists in detecting malignant lesions on mammograms can be improved if an appropriate training intervention is applied after the readers' weakness and strength are identified.

Peer Learning in Radiology: Effect of a Pay-for-Performance Initiative on Clinical Impact and Usage.

OBJECTIVE. The purpose of this article is to assess the effects of a pay-for-performance (PFP) initiative on clinical impact and usage of a radiology peer learning tool. MATERIALS AND METHODS. This retrospective study was performed at a large academic hospital. On May 1, 2017, a peer learning tool was implemented to facilitate radiologist peer feedback including clinical follow-up, positive feedback, and consultation. Subsequently, PFP target numbers for peer learning tool alerts by subspecialty divisions (October 1, 2017) and individual radiologists (October 1, 2018) were set. The primary outcome was report addendum rate (percent of clinical follow-up alerts with addenda), which was a proxy for peer learning tool clinical impact. Secondary outcomes were peer learning tool usage rate (number of peer learning tool alerts per 1000 radiology reports) and proportion of clinical follow-up alerts (percent of clinical follow-ups among all peer learning tool alerts). Outcomes were assessed biweekly using ANOVA and statistical process control analyses. RESULTS. Among 1,265,839 radiology reports from May 1, 2017, to September 29, 2019, a total of 20,902 peer learning tool alerts were generated. The clinical follow-up alert addendum rate was not significantly different between the period before the PFP initiative (9.9%) and the periods including division-wide (8.3%) and individual (7.9%) PFP initiatives (p = .55; ANOVA). Peer learning tool usage increased from 2.2 alerts per 1000 reports before the PFP initiative to 12.6 per 1000 during the division-wide PFP period (5.7-fold increase; 12.6/2.2), to 25.2 in the individual PFP period (11.5-fold increase vs before PFP; twofold increase vs division-wide) (p < .001). The clinical follow-up alert proportion decreased from 37.5% before the PFP initiative, to 34.4% in the division-wide period, to 31.3% in the individual PFP period. CONCLUSION. A PFP initiative improved radiologist engagement in peer learning by marked increase in peer learning tool usage rate without a change in report addendum rate as a proxy for clinical impact.

Am I Ready to Be an Independent Neuroradiologist? Objective Trends in Neuroradiology Fellows' Performance during the Fellowship Year.

BACKGROUND AND PURPOSE: Aside from basic Accreditation Council for Graduate Medical Education guidelines, few metrics are in place to monitor fellows' progress. The purpose of this study was to determine objective trends in neuroradiology fellowship training on-call performance during an academic year. MATERIALS AND METHODS: We retrospectively reviewed the number of cross-sectional neuroimaging studies dictated with complete reports by neuroradiology fellows during independent call. Monthly trends in total call cases, report turnaround times, relationships between volume and report turnaround times, and words addended to preliminary reports by attending neuroradiologists were evaluated with regression models. Monthly variation in frequencies of call-discrepancy macros were assessed via χ2 tests. Changes in frequencies of specific macro use between fellowship semesters were assessed via serial 2-sample tests of proportions. RESULTS: From 2012 to 2017, for 29 fellows, monthly median report turnaround times significantly decreased during the academic year: July (first month) = 79 minutes (95% CI, 71-86 minutes) and June (12th month) = 55 minutes (95% CI, 52-60 minutes; P value = .023). Monthly report turnaround times were inversely correlated with total volumes for CT (r = -0.70, F = 9.639, P value = .011) but not MR imaging. Words addended to preliminary reports, a surrogate measurement of report clarity, slightly improved and discrepancy rates decreased during the last 6 months of fellowship. A nadir for report turnaround times, discrepancy errors, and words addended to reports was seen in December and January. CONCLUSIONS: Progress through fellowship correlates with a decline in report turnaround times and discrepancy rates for cross-sectional neuroimaging call studies and slight improvement in indirect quantitative measurement of report clarity. These metrics can be tracked throughout the academic year, and the midyear would be a logical time point for programs to assess objective progress of fellows and address any deficiencies.