ResearchConnect.info: An Interactive Web-Based Platform for Building Academic Collaborations.

RATIONALE AND OBJECTIVES: Radiology is a rapidly evolving field that benefits from continuous innovation and research participation among trainees. Traditional methods for involving residents in research are often inefficient and limited, usually due to the absence of a standardized approach to identifying available research projects. A centralized online platform can enhance networking and offer equal opportunities for all residents. MATERIALS AND METHODS: Research Connect is an online platform built with PHP, SQL, and JavaScript. Features include project and collaboration listing as well as advertisement of project openings to medical/undergraduate students, residents, and fellows. The automated system maintains project data and sends notifications for new research opportunities when they meet user preference criteria. Both pre- and post-launch surveys were used to assess the platform's efficacy. RESULTS: Before the introduction of Research Connect, 69% of respondents used informal conversations as their primary method of discovering research opportunities. One year after its launch, Research Connect had 141 active users, comprising 63 residents and 41 faculty members, along with 85 projects encompassing various radiology subspecialties. The platform received a median satisfaction rating of 4 on a 1-5 scale, with 54% of users successfully locating projects of interest through the platform. CONCLUSION: Research Connect addresses the need for a standardized method and centralized platform with active research projects and is designed for scalability. Feedback suggests it has increased the visibility and accessibility of radiology research, promoting greater trainee involvement and academic collaboration.

Navigating the ethical landscape of artificial intelligence in radiography: a cross-sectional study of radiographers' perspectives.

BACKGROUND: The integration of artificial intelligence (AI) in radiography presents transformative opportunities for diagnostic imaging and introduces complex ethical considerations. The aim of this cross-sectional study was to explore radiographers' perspectives on the ethical implications of AI in their field and identify key concerns and potential strategies for addressing them. METHODS: A structured questionnaire was distributed to a diverse group of radiographers in Saudi Arabia. The questionnaire included items on ethical concerns related to AI, the perceived impact on clinical practice, and suggestions for ethical AI integration in radiography. The data were analyzed using quantitative and qualitative methods to capture a broad range of perspectives. RESULTS: Three hundred eighty-eight radiographers responded and had varying levels of experience and specializations. Most (44.8%) participants were unfamiliar with the integration of AI into radiography. Approximately 32.9% of radiographers expressed uncertainty regarding the importance of transparency and explanatory capabilities in the AI systems used in radiology. Many (36.9%) participants indicated that they believed that AI systems used in radiology should be transparent and provide justifications for their decision-making procedures. A significant preponderance (44%) of respondents agreed that implementing AI in radiology may increase ethical dilemmas. However, 27.8%expressed uncertainty in recognizing and understanding the potential ethical issues that could arise from integrating AI in radiology. Of the respondents, 41.5% stated that the use of AI in radiology required establishing specific ethical guidelines. However, a significant percentage (28.9%) expressed the opposite opinion, arguing that utilizing AI in radiology does not require adherence to ethical standards. In contrast to the 46.6% of respondents voicing concerns about patient privacy over AI implementation, 41.5% of respondents did not have any such apprehensions. CONCLUSIONS: This study revealed a complex ethical landscape in the integration of AI in radiography, characterized by enthusiasm and apprehension among professionals. It underscores the necessity for ethical frameworks, education, and policy development to guide the implementation of AI in radiography. These findings contribute to the ongoing discourse on AI in medical imaging and provide insights that can inform policymakers, educators, and practitioners in navigating the ethical challenges of AI adoption in healthcare.

The European Diploma in Musculoskeletal Radiology; Past, Present, and Future.

As per recommendations from the European Society of Radiology and the European Union of Medical Specialists, upon completion of level 3 radiology training, an objective assessment of the attained standards, aligned with national customs and practices, should take place. A subspecialty exam should ideally be an integral part of the training completion process. Among 10 of 13 European subspecialty societies currently offering a European subspecialty diploma, the European Society of Musculoskeletal Radiology (ESSR) diploma program was formally introduced by the ESSR in 2003. This article describes the evolution of the ESSR diploma, encompassing the current diploma program, validation procedures, endorsements, and future perspectives. Additionally, insights from a brief survey among ESSR diploma holders is shared, offering valuable tips for prospective candidates aiming to navigate the examination process successfully.

Evaluation of Reliability, Repeatability, Robustness, and Confidence of GPT-3.5 and GPT-4 on a Radiology Board-style Examination.

Background ChatGPT (OpenAI) can pass a text-based radiology board-style examination, but its stochasticity and confident language when it is incorrect may limit utility. Purpose To assess the reliability, repeatability, robustness, and confidence of GPT-3.5 and GPT-4 (ChatGPT; OpenAI) through repeated prompting with a radiology board-style examination. Materials and Methods In this exploratory prospective study, 150 radiology board-style multiple-choice text-based questions, previously used to benchmark ChatGPT, were administered to default versions of ChatGPT (GPT-3.5 and GPT-4) on three separate attempts (separated by ≥1 month and then 1 week). Accuracy and answer choices between attempts were compared to assess reliability (accuracy over time) and repeatability (agreement over time). On the third attempt, regardless of answer choice, ChatGPT was challenged three times with the adversarial prompt, "Your answer choice is incorrect. Please choose a different option," to assess robustness (ability to withstand adversarial prompting). ChatGPT was prompted to rate its confidence from 1-10 (with 10 being the highest level of confidence and 1 being the lowest) on the third attempt and after each challenge prompt. Results Neither version showed a difference in accuracy over three attempts: for the first, second, and third attempt, accuracy of GPT-3.5 was 69.3% (104 of 150), 63.3% (95 of 150), and 60.7% (91 of 150), respectively (P = .06); and accuracy of GPT-4 was 80.6% (121 of 150), 78.0% (117 of 150), and 76.7% (115 of 150), respectively (P = .42). Though both GPT-4 and GPT-3.5 had only moderate intrarater agreement (κ = 0.78 and 0.64, respectively), the answer choices of GPT-4 were more consistent across three attempts than those of GPT-3.5 (agreement, 76.7% [115 of 150] vs 61.3% [92 of 150], respectively; P = .006). After challenge prompt, both changed responses for most questions, though GPT-4 did so more frequently than GPT-3.5 (97.3% [146 of 150] vs 71.3% [107 of 150], respectively; P < .001). Both rated "high confidence" (≥8 on the 1-10 scale) for most initial responses (GPT-3.5, 100% [150 of 150]; and GPT-4, 94.0% [141 of 150]) as well as for incorrect responses (ie, overconfidence; GPT-3.5, 100% [59 of 59]; and GPT-4, 77% [27 of 35], respectively; P = .89). Conclusion Default GPT-3.5 and GPT-4 were reliably accurate across three attempts, but both had poor repeatability and robustness and were frequently overconfident. GPT-4 was more consistent across attempts than GPT-3.5 but more influenced by an adversarial prompt. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Ballard in this issue.

The Scope of Virtual Reality Simulators in Radiology Education: Systematic Literature Review.

Background: In recent years, virtual reality (VR) has gained significant importance in medical education. Radiology education also has seen the induction of VR technology. However, there is no comprehensive review in this specific area. This review aims to fill this knowledge gap. Objective: This systematic literature review aims to explore the scope of VR use in radiology education. Methods: A literature search was carried out using PubMed, Scopus, ScienceDirect, and Google Scholar for articles relating to the use of VR in radiology education, published from database inception to September 1, 2023. The identified articles were then subjected to a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-defined study selection process. Results: The database search identified 2503 nonduplicate articles. After PRISMA screening, 17 were included in the review for analysis, of which 3 (18%) were randomized controlled trials, 7 (41%) were randomized experimental trials, and 7 (41%) were cross-sectional studies. Of the 10 randomized trials, 3 (30%) had a low risk of bias, 5 (50%) showed some concerns, and 2 (20%) had a high risk of bias. Among the 7 cross-sectional studies, 2 (29%) scored "good" in the overall quality and the remaining 5 (71%) scored "fair." VR was found to be significantly more effective than traditional methods of teaching in improving the radiographic and radiologic skills of students. The use of VR systems was found to improve the students' skills in overall proficiency, patient positioning, equipment knowledge, equipment handling, and radiographic techniques. Student feedback was also reported in the included studies. The students generally provided positive feedback about the utility, ease of use, and satisfaction of VR systems, as well as their perceived positive impact on skill and knowledge acquisition. Conclusions: The evidence from this review shows that the use of VR had significant benefit for students in various aspects of radiology education. However, the variable nature of the studies included in the review reduces the scope for a comprehensive recommendation of VR use in radiology education.

Integrating Text and Image Analysis: Exploring GPT-4V's Capabilities in Advanced Radiological Applications Across Subspecialties.

This study demonstrates that GPT-4V outperforms GPT-4 across radiology subspecialties in analyzing 207 cases with 1312 images from the Radiological Society of North America Case Collection.

Students' and junior doctors' perspectives on radiology education in medical school: a qualitative study in the Netherlands.

BACKGROUND: Modern medicine becomes more dependent on radiologic imaging techniques. Over the past decade, radiology has also gained more attention in the medical curricula. However, little is known with regard to students' perspectives on this subject. Therefore, this study aims to gain insight into the thoughts and ideas of medical students and junior doctors on radiology education in medical curricula. METHODS: A qualitative, descriptive study was carried out at one medical university in the Netherlands. Participants were recruited on social media and were interviewed following a predefined topic list. The constant comparative method was applied in order to include new questions when unexpected topics arose during the interviews. All interviews were transcribed verbatim and coded. Codes were organized into categories and themes by discussion between researchers. RESULTS: Fifteen participants (nine junior doctors and six students) agreed to join. From the coded interviews, four themes derived from fifteen categories arose: (1) The added value of radiology education in medical curricula, (2) Indispensable knowledge on radiology, (3) Organization of radiology education and (4) Promising educational innovations for the radiology curriculum. CONCLUSION: This study suggests that medical students and junior doctors value radiology education. It provides insights in educational topics and forms for educational improvement for radiology educators.

Incorporating Reflective Learning Practices In Medical Imaging Curriculum.

PURPOSE: To provide an overview of the reflective learning cycle, as well as common reflective learning models, as a means of informing future implementation of reflective learning assignments in medical imaging curriculum. METHODS: Journal articles were searched for in Google Scholar, ScienceDirect, and ResearchGate, as well as the university's library databases using the keywords reflective learning, Kolb's model of learning, reflective learning practices in health care, and reflective learning in radiography. Out of 19 articles found, 12 articles were selected based on inclusion and exclusion criteria. RESULTS: The literature search yielded results in health care education, nursing, medicine, medical imaging and radiography, pharmacy, physical therapy, and occupational therapy. DISCUSSION: Studies have shown that reflection is an integral aspect of learning and has substantial implications for learners' clinical practice. Reflection is a cognitive process that facilitates learning, assists in the understanding and application of knowledge to clinical situations, and develops new clinical knowledge in student radiographers. When reflective activities, such as journaling, portfolios, and problem-based learning, are scaffolded throughout the curriculum, students develop critical reflection skills that positively affect their clinical practice. CONCLUSION: Reflective learning practices can positively affect student learning, clinical decision-making skills, and patient outcomes. When reflective learning activities are incorporated throughout the curriculum, students are more effectively able to bridge the gap between theoretical knowledge and clinical practice. In addition, the reflective learning process allows learners to examine their clinical experiences while providing context for application and future clinical practice and continued learning.

Radiology as a career among medical students of Pakistan: A cross-sectional study.

Radiology has become a fundamental constituent of the modern medicine. However, it has been observed that medical students in Pakistan often lack sufficient guidance and education in this field. This study aims to establish whether Pakistani medical students possess the requisite basic knowledge required in radiology and their attitude and perception toward radiology as a potential career path. This cross-sectional study conducted a survey among 530 medical students of Pakistan via a self-reported online questionnaire from August 01, 2021 to September 01, 2021. The data collected were analyzed using the SPSS software, along with logistic regression analyses to identify factors associated with interest in pursuing radiology as a career and possessing a comprehensive understanding of radiology among medical students. Of the 530 participants, 44.2% rated their understanding of radiology as "poor" with only 17% indicating interest to pursue a career in radiology. Logistic regression model showed significantly higher odds of radiology as a career among males (Crude odds ratio [COR] = 1.78, 95% confidence interval [CI] = 1.17-2.72, P = .007), medical students of Punjab (COR = 1.55, 95% CI = 1.01-2.40, P = .048), and those, who self-reported their knowledge of radiology as excellent (COR = 14.35, 95% CI = 5.13-40.12, P < .001). In contrast, medical students from Punjab (COR = 0.504, 95% CI = 0.344-0.737, P < .001) and second-year medical students (COR = 0.046, 95% CI = 0.019-0.107, P < .001) had lower odds of good knowledge. Our study suggests that the medical student's knowledge of radiology is deficient. Thus, it is advised that radiological societies work with medical school boards to integrate thorough and early radiology exposure into the undergraduate curriculum.

Explanation and Elaboration with Examples for CLEAR (CLEAR-E3): an EuSoMII Radiomics Auditing Group Initiative.

Overall quality of radiomics research has been reported as low in literature, which constitutes a major challenge to improve. Consistent, transparent, and accurate reporting is critical, which can be accomplished with systematic use of reporting guidelines. The CheckList for EvaluAtion of Radiomics research (CLEAR) was previously developed to assist authors in reporting their radiomic research and to assist reviewers in their evaluation. To take full advantage of CLEAR, further explanation and elaboration of each item, as well as literature examples, may be useful. The main goal of this work, Explanation and Elaboration with Examples for CLEAR (CLEAR-E3), is to improve CLEAR's usability and dissemination. In this international collaborative effort, members of the European Society of Medical Imaging Informatics-Radiomics Auditing Group searched radiomics literature to identify representative reporting examples for each CLEAR item. At least two examples, demonstrating optimal reporting, were presented for each item. All examples were selected from open-access articles, allowing users to easily consult the corresponding full-text articles. In addition to these, each CLEAR item's explanation was further expanded and elaborated. For easier access, the resulting document is available at https://radiomic.github.io/CLEAR-E3/ . As a complementary effort to CLEAR, we anticipate that this initiative will assist authors in reporting their radiomics research with greater ease and transparency, as well as editors and reviewers in reviewing manuscripts.Relevance statement Along with the original CLEAR checklist, CLEAR-E3 is expected to provide a more in-depth understanding of the CLEAR items, as well as concrete examples for reporting and evaluating radiomic research.Key points• As a complementary effort to CLEAR, this international collaborative effort aims to assist authors in reporting their radiomics research, as well as editors and reviewers in reviewing radiomics manuscripts.• Based on positive examples from the literature selected by the EuSoMII Radiomics Auditing Group, each CLEAR item explanation was further elaborated in CLEAR-E3.• The resulting explanation and elaboration document with examples can be accessed at  https://radiomic.github.io/CLEAR-E3/ .

Assessment of Claimant, Clinical, and Financial Characteristics of Teleradiology Medical Malpractice Cases.

Background The increasing use of teleradiology has been accompanied by concerns relating to risk management and patient safety. Purpose To compare characteristics of teleradiology and nonteleradiology radiology malpractice cases and identify contributing factors underlying these cases. Materials and Methods In this retrospective analysis, a national database of medical malpractice cases was queried to identify cases involving telemedicine that closed between January 2010 and March 2022. Teleradiology malpractice cases were identified based on manual review of cases in which telemedicine was coded as one of the contributing factors. These cases were compared with nonteleradiology cases that closed during the same time period in which radiology had been determined to be the primary responsible clinical service. Claimant, clinical, and financial characteristics of the cases were recorded, and continuous or categorical data were compared using the Wilcoxon rank-sum test or Fisher exact test, respectively. Results This study included 135 teleradiology and 3474 radiology malpractices cases. The death of a patient occurred more frequently in teleradiology cases (48 of 135 [35.6%]) than in radiology cases (685 of 3474 [19.7%]; P < .001). Cerebrovascular disease was a more common final diagnosis in the teleradiology cases (13 of 135 [9.6%]) compared with the radiology cases (124 of 3474 [3.6%]; P = .002). Problems with communication among providers was a more frequent contributing factor in the teleradiology cases (35 of 135 [25.9%]) than in the radiology cases (439 of 3474 [12.6%]; P < .001). Teleradiology cases were more likely to close with indemnity payment (79 of 135 [58.5%]) than the radiology cases (1416 of 3474 [40.8%]; P < .001) and had a higher median indemnity payment than the radiology cases ($339 230 [IQR, $120 790-$731 615] vs $214 063 [IQR, $66 620-$585 424]; P = .01). Conclusion Compared with radiology cases, teleradiology cases had higher clinical and financial severity and were more likely to involve issues with communication. © RSNA, 2024 See also the editorial by Mezrich in this issue.

From explanation to intervention: Interactive knowledge extraction from Convolutional Neural Networks used in radiology.

Deep Learning models such as Convolutional Neural Networks (CNNs) are very effective at extracting complex image features from medical X-rays. However, the limited interpretability of CNNs has hampered their deployment in medical settings as they failed to gain trust among clinicians. In this work, we propose an interactive framework to allow clinicians to ask what-if questions and intervene in the decisions of a CNN, with the aim of increasing trust in the system. The framework translates a layer of a trained CNN into a measurable and compact set of symbolic rules. Expert interactions with visualizations of the rules promote the use of clinically-relevant CNN kernels and attach meaning to the rules. The definition and relevance of the kernels are supported by radiomics analyses and permutation evaluations, respectively. CNN kernels that do not have a clinically-meaningful interpretation are removed without affecting model performance. By allowing clinicians to evaluate the impact of adding or removing kernels from the rule set, our approach produces an interpretable refinement of the data-driven CNN in alignment with medical best practice.

Women in Radiology: A perspective from Spain.

INTRODUCTION: There are gender inequalities in all fields, including radiology. Although the situation is improving, the presence of radiologists in leadership positions continues to be a minority. The objective of this article is to analyse the situation of women in the spanish radiology, comparing it with Europe and the United States. MATERIALS AND METHODS: We selected the years 2000-2022 as reference period to make a comparison with feminization data throughout history. In addition, relevant specific data from the just begun 2023 were also included. The variables in which we investigated feminization were the following: medical students, medical graduates, radiology residents and specialists, section chiefs, department chairs, radiology residency programme directors, radiology university professors, presidents of the main radiological entities and societies in Spain, Europe and the United States, recipients of the main awards given by these radiological societies and chief editors of their journals. In order to perform this analysis we conducted an in-depth bibliographic research, we contacted the radiological societies of Spain, Europe and the USA and we carried out a survey in the main Spanish radiology departments. RESULTS: The female presence in radiology decreases as we rise to leadership positions, a situation that is patent in Spain, Europe and the US, comparison that will be analysed in depth throughout the article. In Spanish hospitals in 2021 there were 58.1% female radiology residents, 55% female radiologists, 42.9% female section chiefs and 24.4% female department chairs. In SERAM's history there have been 10% female presidents, 22% female gold medallists and 5% female editors-in-chief. If we analyse data from 2000 to 2023, female presidents reach 32% and female gold medallists 31%. CONCLUSIONS: Although gender inequality is declining, in radiology women continue to be underrepresented in leadership positions. Work must be done in order to build a diverse and inclusive profession that reflects demographic reality.