Fostering Organizational Excellence through Inclusive Leadership: Practical Guide for Radiology Leaders.

Inclusive leadership styles value team members, invite diverse perspectives, and recognize and support the contributions of employees. The authors provide guidance to radiology leaders interested in developing inclusive leadership skills and competencies to improve workforce recruitment and retention and unlock the potential of a rapidly diversifying health care workforce. As health care organizations look to attract the best and brightest talent, they will be increasingly recruiting millennial and Generation Z employees, who belong to the most diverse generations in American history. Additionally, radiology departments currently face critical workforce shortages in radiologists, radiology technicians, staff, and advanced practice providers. In the context of these shortages, the costs of employee turnover have emphasized the need for radiology leaders to develop leadership behaviors that promote recruitment and retention. Radiology department leaders who perceive and treat valued employees as replaceable commodities will be forced to deal with the extremely high costs associated with recruitment and training, decreased morale, and increased burnout. The authors review inclusive versus exclusive leadership styles, describe key attributes and skills of inclusive leaders, provide radiology leaders with concrete methods to make their organizations more inclusive, and outline key steps in change management. By adopting and implementing inclusive leadership strategies, radiology groups can position themselves to succeed in rapidly diversifying health care environments. ©RSNA, 2024 See the invited commentary by Siewert in this issue.

'Humans think outside the pixels' - Radiologists' perceptions of using artificial intelligence for breast cancer detection in mammography screening in a clinical setting.

OBJECTIVE: This study aimed to explore radiologists' views on using an artificial intelligence (AI) tool named ScreenTrustCAD with Philips equipment) as a diagnostic decision support tool in mammography screening during a clinical trial at Capio Sankt Göran Hospital, Sweden. METHODS: We conducted semi-structured interviews with seven breast imaging radiologists, evaluated using inductive thematic content analysis. RESULTS: We identified three main thematic categories: AI in society, reflecting views on AI's contribution to the healthcare system; AI-human interactions, addressing the radiologists' self-perceptions when using the AI and its potential challenges to their profession; and AI as a tool among others. The radiologists were generally positive towards AI, and they felt comfortable handling its sometimes-ambiguous outputs and erroneous evaluations. While they did not feel that it would undermine their profession, they preferred using it as a complementary reader rather than an independent one. CONCLUSION: The results suggested that breast radiology could become a launch pad for AI in healthcare. We recommend that this exploratory work on subjective perceptions be complemented by quantitative assessments to generalize the findings.

Investigation of Radiologist Diagnostic Difficulty Prediction Without CT Images.

For better collaboration among radiologists, the interpretation workload should be evaluated, considering the difference in difficulty for interpreting each case. However, objective evaluation of difficulty is challenging. This study proposes a multimodal classifier of structural and textual data to predict difficulty based on order information and patient data without using images. The classifier showed performance with a specificity of 0.9 and an accuracy of 0.7.

Interventional radiologists and OBLs: An excellent match.

While office-based laboratories (OBLs) have been increasing in popularity, only a small proportion of the current interventional radiology (IR) workforce works in an OBL. With the relative lack of an IR presence in OBLs compared to other endovascular specialists, combined with the growth of the OBL space, the presence of IR within OBLs will likely increase in the coming years. This article addresses the value interventional radiologists (IRs) can bring to the OBL, with primary impacts being the ability to impact a larger proportion of the population than is traditionally cared for in most hospital settings, the ability to positively influence multidisciplinary care teams and the financial leverage inherent in procedural diversification not readily afforded by other specialists working in the OBL space. IR-specific pitfalls in the OBL space are also addressed, including difficulties in obtaining patient referrals, investor relationships, and group practice arrangements. Despite potential challenges, IRs have a lot to offer within the OBL space, and conversely, the OBL space provides a mechanism for IRs to increase their reach and improve career longevity.

Sole operator: Owning and Operating an Independent Office-Based Lab.

Owning and operating an Office-Based Lab (OBL) creates a unique career, combining the privilege of practicing Interventional Radiology (IR) with the creativity and excitement of running a complex business. No business is more complicated than the American Healthcare system, with a combination of necessary operational systems, government and commercial reimbursement, local and national regulations, an ever-changing landscape, and various patient populations; the business is always shifting. No field is as complex and exciting as Interventional Radiology, with advanced clinical and technical expertise, device development, rocedural ingenuity, and the ability to solve complex medical problems with elegant solutions. A sole owner and operator in an OBL has full autotomy, and thus full responsibility for the medical and business aspects of the practice.

Transitioning from Hospital-based to Office-Based Practice.

The decision to change your career path from a hospital-based practice, whether it's from being a hospital employee or a member of a private practice, can be an emotionally draining choice that is complex and overwhelming to say the least. There are many factors to consider before making this switch, but most importantly, one must realize it may be the hardest but most rewarding work in your career. While the physical, emotional and financial stresses placed on you while developing a practice can be rather demanding, on the flip side, if done correctly and the practice thrives, it can be a change that will bring you great pride and satisfaction, as well as personal reward and freedom.

Network analysis of occupational stress and job satisfaction among radiologists.

Background: Occupational stress and job satisfaction significantly impact the well-being and performance of healthcare professionals, including radiologists. Understanding the complex interplay between these factors through network analysis can provide valuable insights into intervention strategies to enhance workplace satisfaction and productivity. Method: In this study, a convenience sampling method was used to recruit 312 radiologists for participation. Data on socio-demographic characteristics, job satisfaction measured by the Minnesota job satisfaction questionnaire revised short version (MJSQ-RSV), and occupational stress assessed using the occupational stress scale. Network analysis was employed to analyze the data in this study. Results: The network analysis revealed intricate patterns of associations between occupational stress and job satisfaction symptoms among radiologists. Organizational management and occupational interests emerged as crucial nodes in the network, indicating strong relationships within these domains. Additionally, intrinsic satisfaction was identified as a central symptom with high connectivity in the network structure. The stability analysis demonstrated robustness in the network edges and centrality metrics, supporting the reliability of the findings. Conclusion: This study sheds light on the complex relationships between occupational stress and job satisfaction in radiologists, offering valuable insights for targeted interventions and support strategies to promote well-being and job satisfaction in healthcare settings.

Characterizing Sentinel Lymph Node Status in Breast Cancer Patients Using a Deep-Learning Model Compared With Radiologists' Analysis of Grayscale Ultrasound and Lymphosonography.

ABSTRACT: The objective of the study was to use a deep learning model to differentiate between benign and malignant sentinel lymph nodes (SLNs) in patients with breast cancer compared to radiologists' assessments.Seventy-nine women with breast cancer were enrolled and underwent lymphosonography and contrast-enhanced ultrasound (CEUS) examination after subcutaneous injection of ultrasound contrast agent around their tumor to identify SLNs. Google AutoML was used to develop image classification model. Grayscale and CEUS images acquired during the ultrasound examination were uploaded with a data distribution of 80% for training/20% for testing. The performance metric used was area under precision/recall curve (AuPRC). In addition, 3 radiologists assessed SLNs as normal or abnormal based on a clinical established classification. Two-hundred seventeen SLNs were divided in 2 for model development; model 1 included all SLNs and model 2 had an equal number of benign and malignant SLNs. Validation results model 1 AuPRC 0.84 (grayscale)/0.91 (CEUS) and model 2 AuPRC 0.91 (grayscale)/0.87 (CEUS). The comparison between artificial intelligence (AI) and readers' showed statistical significant differences between all models and ultrasound modes; model 1 grayscale AI versus readers, P = 0.047, and model 1 CEUS AI versus readers, P < 0.001. Model 2 r grayscale AI versus readers, P = 0.032, and model 2 CEUS AI versus readers, P = 0.041.The interreader agreement overall result showed κ values of 0.20 for grayscale and 0.17 for CEUS.In conclusion, AutoML showed improved diagnostic performance in balance volume datasets. Radiologist performance was not influenced by the dataset's distribution.

Radiologists' perceptions on AI integration: An in-depth survey study.

PURPOSE: To assess the perceptions and attitudes of radiologists toward the adoption of artificial intelligence (AI) in clinical practice. METHODS: A survey was conducted among members of the SIRM Lombardy. Radiologists' attitudes were assessed comprehensively, covering satisfaction with AI-based tools, propensity for innovation, and optimism for the future. The questionnaire consisted of two sections: the first gathered demographic and professional information using categorical responses, while the second evaluated radiologists' attitudes toward AI through Likert-type responses ranging from 1 to 5 (with 1 representing extremely negative attitudes, 3 indicating a neutral stance, and 5 reflecting extremely positive attitudes). Questionnaire refinement involved an iterative process with expert panels and a pilot phase to enhance consistency and eliminate redundancy. Exploratory data analysis employed descriptive statistics and visual assessment of Likert plots, supported by non-parametric tests for subgroup comparisons for a thorough analysis of specific emerging patterns. RESULTS: The survey yielded 232 valid responses. The findings reveal a generally optimistic outlook on AI adoption, especially among young radiologist (<30) and seasoned professionals (>60, p<0.01). However, while 36.2 % (84 out 232) of subjects reported daily use of AI-based tools, only a third considered their contribution decisive (30 %, 25 out of 84). AI literacy varied, with a notable proportion feeling inadequately informed (36 %, 84 out of 232), particularly among younger radiologists (46 %, p < 0.01). Positive attitudes towards the potential of AI to improve detection, characterization of anomalies and reduce workload (positive answers > 80 %) and were consistent across subgroups. Radiologists' opinions were more skeptical about the role of AI in enhancing decision-making processes, including the choice of further investigation, and in personalized medicine in general. Overall, respondents recognized AI's significant impact on the radiology profession, viewing it as an opportunity (61 %, 141 out of 232) rather than a threat (18 %, 42 out of 232), with a majority expressing belief in AI's relevance to future radiologists' career choices (60 %, 139 out of 232). However, there were some concerns, particularly among breast radiologists (20 of 232 responders), regarding the potential impact of AI on the profession. Eighty-four percent of the respondents consider the final assessment by the radiologist still to be essential. CONCLUSION: Our results indicate an overall positive attitude towards the adoption of AI in radiology, though this is moderated by concerns regarding training and practical efficacy. Addressing AI literacy gaps, especially among younger radiologists, is essential. Furthermore, proactively adapting to technological advancements is crucial to fully leverage AI's potential benefits. Despite the generally positive outlook among radiologists, there remains significant work to be done to enhance the integration and widespread use of AI tools in clinical practice.

Pioneers of progress: Documenting the legacy of underrepresented radiologists.

PURPOSE: This study aims to illuminate the enduring contributions of underrepresented pioneers in radiology, emphasizing their resilience, innovations, and the significant barriers they overcame. By weaving their achievements into the broader narrative of medical science, this research highlights the critical role of diversity and progress in the evolution of radiology. HISTORICAL EXPLORATION: This narrative review chronicles the significant contributions of underrepresented radiologists from the early 20th century to the present. By synthesizing historical data, biographical sketches, and contemporary medical literature, we highlight the pivotal roles these pioneers have played in advancing radiology. Their groundbreaking work not only enhanced medical imaging technologies and practices but also championed the cause of diversity and inclusion within the field. These stories of perseverance and innovation underscore the ongoing need for an inclusive approach in the medical community, reflecting on how diversity has shaped and will continue to influence the evolution of radiology. FINDINGS AND CONCLUSION: The study identifies several pivotal figures, such as Marcus F. Wheatland, the first known African American radiologist, and Ivy O. Roach Brooks, the first woman to lead a radiology department at a major U.S. hospital. It explores their wide-ranging contributions from clinical practice and education to leadership and advocacy for diversity within the medical profession. The legacies of these radiologists illuminate not just their individual accomplishments but also reflect the broader struggle for equality and representation in the medical field. Their determination and excellence have paved the way for future generations, significantly enhancing the inclusivity and diversity of the radiology field. CLINICAL RELEVANCE AND APPLICATION: Understanding the contributions of these underrepresented radiologists enriches the field's perspective on diversity, equity, and inclusion. Highlighting these pioneers underscores the importance of mentorship, representation, and advocacy in creating an environment where all talented individuals can thrive. Insights from this historical analysis are crucial for shaping future policies and practices in radiology and medical education, ensuring the continuation of these trailblazers' inspiring legacy.

The clinical value of artificial intelligence in assisting junior radiologists in thyroid ultrasound: a multicenter prospective study from real clinical practice.

BACKGROUND: This study is to propose a clinically applicable 2-echelon (2e) diagnostic criteria for the analysis of thyroid nodules such that low-risk nodules are screened off while only suspicious or indeterminate ones are further examined by histopathology, and to explore whether artificial intelligence (AI) can provide precise assistance for clinical decision-making in the real-world prospective scenario. METHODS: In this prospective study, we enrolled 1036 patients with a total of 2296 thyroid nodules from three medical centers. The diagnostic performance of the AI system, radiologists with different levels of experience, and AI-assisted radiologists with different levels of experience in diagnosing thyroid nodules were evaluated against our proposed 2e diagnostic criteria, with the first being an arbitration committee consisting of 3 senior specialists and the second being cyto- or histopathology. RESULTS: According to the 2e diagnostic criteria, 1543 nodules were classified by the arbitration committee, and the benign and malignant nature of 753 nodules was determined by pathological examinations. Taking pathological results as the evaluation standard, the sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) of the AI systems were 0.826, 0.815, 0.821, and 0.821. For those cases where diagnosis by the Arbitration Committee were taken as the evaluation standard, the sensitivity, specificity, accuracy, and AUC of the AI system were 0.946, 0.966, 0.964, and 0.956. Taking the global 2e diagnostic criteria as the gold standard, the sensitivity, specificity, accuracy, and AUC of the AI system were 0.868, 0.934, 0.917, and 0.901, respectively. Under different criteria, AI was comparable to the diagnostic performance of senior radiologists and outperformed junior radiologists (all P < 0.05). Furthermore, AI assistance significantly improved the performance of junior radiologists in the diagnosis of thyroid nodules, and their diagnostic performance was comparable to that of senior radiologists when pathological results were taken as the gold standard (all p > 0.05). CONCLUSIONS: The proposed 2e diagnostic criteria are consistent with real-world clinical evaluations and affirm the applicability of the AI system. Under the 2e criteria, the diagnostic performance of the AI system is comparable to that of senior radiologists and significantly improves the diagnostic capabilities of junior radiologists. This has the potential to reduce unnecessary invasive diagnostic procedures in real-world clinical practice.

Comparison of the Size Measurement of Gallbladder Polyps by Three Different Radiologists in Abdominal Ultrasonography.

BACKGROUND: There is little information regarding the size measurement differences in gallbladder (GB) polyps performed by different radiologists on abdominal ultrasonography (US). AIM: To reveal the differences in GB polyp size measurements performed by different radiologists on abdominal US. METHODS: From June to September 2022, the maximum diameter of 228 GB polyps was measured twice on abdominal US by one of three radiologists (a third-year radiology resident [reader A], a radiologist with 7 years of experience in abdominal US [reader B], and an abdominal radiologist with 8 years of experience in abdominal US [reader C]). Intra-reader agreements for polyp size measurements were assessed by intraclass correlation coefficient (ICC). A Bland-Altman plot was used to visualize the differences between the first and second size measurements in each reader. RESULTS: Reader A, reader B, and reader C evaluated 65, 77, and 86 polyps, respectively. The mean size of measured 228 GB polyps was 5.0 ± 1.9 mm. Except for the case where reader A showed moderate intra-reader agreement (0.726) for polyps with size ≤ 5 mm, all readers showed an overall high intra-reader reliability (reader A, ICC = 0.859; reader B, ICC = 0.947, reader C, ICC = 0.948), indicative of good and excellent intra-reader agreements. The 95% limit of agreement of reader A, B, and C was 1.9 mm of the mean in all three readers. CONCLUSIONS: GB polyp size measurement on abdominal US showed good or excellent intra-reader agreements. However, size changes of approximately less than 1.9 mm should be interpreted carefully because these may be within the measurement error.

Comparative analysis of carotid artery stenting practice and outcomes across operator specialties: insights from a prespecified subanalysis of a large real-world patient cohort (ROADSAVER Study).

BACKGROUND: In contemporary clinical practice, carotid artery stenting (CAS) is increasingly becoming a multispecialty field, joining operators of various training backgrounds, which bring forth their unique expertise, patient management philosophies, and procedural preferences. The best practices and approaches, however, are still debated. Therefore, real-world insights on different operator preferences and related outcomes are of utmost value, yet still rather scarce in the available literature. METHODS: Using the data collected in the ROADSAVER observational, European multicenter CAS study, a prespecified comparative analysis evaluating the impact of the operator's specialization was performed. We used major adverse event (MAE) rate at 30-day follow-up, defined as the cumulative incidence of any death or stroke, and its components as outcome measures. RESULTS: A total of 1965 procedures were analyzed; almost half 878 (44.7%) were performed by radiologists (interventional/neuro), 717 (36.5%) by cardiologists or angiologists, and 370 (18.8%) by surgeons (vascular/neuro). Patients treated by surgeons were the oldest (72.9±8.5), while radiologists treated most symptomatic patients (58.1%) and more often used radial access (37.2%). The 30-day MAE incidence achieved by cardiologists/angiologists was 2.0%, radiologists 2.5%, and surgeons 1.9%; the observed differences in rates were statistically not-significant (P=0.7027), even when adjusted for baseline patient/lesion and procedural disparities across groups. The corresponding incidence rates for death from any cause were 1.0%, 0.8%, and 0.3%, P=0.4880, and for any stroke: 1.4%, 2.3%, and 1.9%, P=0.4477, respectively. CONCLUSIONS: Despite the disparities in patient selection and procedural preferences, the outcomes achieved by different specialties in real-world, contemporary CAS practice remain similar when using modern devices and techniques.

Comparing Diagnostic Accuracy of Radiologists versus GPT-4V and Gemini Pro Vision Using Image Inputs from Diagnosis Please Cases.

Background The diagnostic abilities of multimodal large language models (LLMs) using direct image inputs and the impact of the temperature parameter of LLMs remain unexplored. Purpose To investigate the ability of GPT-4V and Gemini Pro Vision in generating differential diagnoses at different temperatures compared with radiologists using Radiology Diagnosis Please cases. Materials and Methods This retrospective study included Diagnosis Please cases published from January 2008 to October 2023. Input images included original images and captures of the textual patient history and figure legends (without imaging findings) from PDF files of each case. The LLMs were tasked with providing three differential diagnoses, repeated five times at temperatures 0, 0.5, and 1. Eight subspecialty-trained radiologists solved cases. An experienced radiologist compared generated and final diagnoses, considering the result correct if the generated diagnoses included the final diagnosis after five repetitions. Accuracy was assessed across models, temperatures, and radiology subspecialties, with statistical significance set at P < .007 after Bonferroni correction for multiple comparisons across the LLMs at the three temperatures and with radiologists. Results A total of 190 cases were included in neuroradiology (n = 53), multisystem (n = 27), gastrointestinal (n = 25), genitourinary (n = 23), musculoskeletal (n = 17), chest (n = 16), cardiovascular (n = 12), pediatric (n = 12), and breast (n = 5) subspecialties. Overall accuracy improved with increasing temperature settings (0, 0.5, 1) for both GPT-4V (41% [78 of 190 cases], 45% [86 of 190 cases], 49% [93 of 190 cases], respectively) and Gemini Pro Vision (29% [55 of 190 cases], 36% [69 of 190 cases], 39% [74 of 190 cases], respectively), although there was no evidence of a statistically significant difference after Bonferroni adjustment (GPT-4V, P = .12; Gemini Pro Vision, P = .04). The overall accuracy of radiologists (61% [115 of 190 cases]) was higher than that of Gemini Pro Vision at temperature 1 (T1) (P < .001), while no statistically significant difference was observed between radiologists and GPT-4V at T1 after Bonferroni adjustment (P = .02). Radiologists (range, 45%-88%) outperformed the LLMs at T1 (range, 24%-75%) in most subspecialties. Conclusion Using direct radiologic image inputs, GPT-4V and Gemini Pro Vision showed improved diagnostic accuracy with increasing temperature settings. Although GPT-4V slightly underperformed compared with radiologists, it nonetheless demonstrated promising potential as a supportive tool in diagnostic decision-making. © RSNA, 2024 See also the editorial by Nishino and Ballard in this issue.