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In the rapidly evolving digital radiology landscape, a surge in solutions has emerged including more than 500 artificial intelligence applications that have received 510 k clearance by the FDA. Moreover, there is an extensive number of non-regulated applications, specifically designed to enhance workflow efficiency within radiology departments. These efficiency applications offer tremendous opportunities to resolve operational pain points and improve efficiency for radiology practices worldwide. However, selecting the most effective workflow efficiency applications presents a major challenge due to the multitude of available solutions and unclear evaluation criteria. In this article, we share our perspective on how to structure the broad field of workflow efficiency applications and how to objectively assess individual solutions. Along the different stages of the radiology workflow, we highlight 31 key operational pain points that radiology practices face and match them with features of workflow efficiency apps aiming to address them. A framework to guide practices in assessing and curating workflow efficiency applications is introduced, addressing key dimensions, including a solution's pain point coverage, efficiency claim strength, evidence and credibility, ease of integration, and usability. We apply this framework in a large-scale analysis of workflow efficiency applications in the market, differentiating comprehensive workflow efficiency ecosystems seeking to address a multitude of pain points through a unified solution from workflow efficiency niche apps following a targeted approach to address individual pain points. Furthermore, we propose an approach to quantify the financial benefits generated by different types of applications that can be leveraged for return-on-investment calculations., Competing Interests: Declarations. Competing Interests: Prateek Bharadwaj, Steven L. Blumer, and Franziska Lobig are employees of Bayer AG. Michael Berger is an employee of Simon-Kucher & Partners Strategy & Marketing Consultants GmbH, a consulting firm which has received consulting fees from Bayer AG., (© 2024. The Author(s).)

The rapid evolution of artificial intelligence (AI), particularly in deep learning, has significantly impacted radiology, introducing an array of AI solutions for interpretative tasks. This paper provides radiology departments with a practical guide for selecting and integrating AI solutions, focusing on interpretative tasks that require the active involvement of radiologists. Our approach is not to list available applications or review scientific evidence, as this information is readily available in previous studies; instead, we concentrate on the essential factors radiology departments must consider when choosing AI solutions. These factors include clinical relevance, performance and validation, implementation and integration, clinical usability, costs and return on investment, and regulations, security, and privacy. We illustrate each factor with hypothetical scenarios to provide a clearer understanding and practical relevance. Through our experience and literature review, we provide insights and a practical roadmap for radiologists to navigate the complex landscape of AI in radiology. We aim to assist in making informed decisions that enhance diagnostic precision, improve patient outcomes, and streamline workflows, thus contributing to the advancement of radiological practices and patient care., Competing Interests: Conflict of interest disclosure: Deniz Alis is the CEO and co-founder of Hevi AI Health Tech. The authors declared no conflicts of interest.

Improving the status of women in radiology is crucial to better work environments. There is strong evidence in the business world that women leaders improve the workplace by making it more financially viable and by increasing collaboration, job satisfaction, and engagement. Diverse leadership fosters innovation, and women approach problem-solving with unique insights and collaborative styles. Gender diversity in leadership correlates with improved patient outcomes because women leaders prioritize patient-centered care and communication. Women create sustainable, productive work and improve radiology. Women serve as powerful role models, inspiring the next generation of women in radiology and addressing gender disparities. Increasing women leaders in radiology is essential to increase the number of women in radiology. This article summarizes many challenges women face when taking leadership roles: organizational biases prioritizing male viewpoints and marginalizing women's voices and contributions, a lack of role models, a lack of time ("second shift"), a lack of confidence, a lack of interest or perceived benefit, a lack of support, burnout, and previous poor experiences. While systemic issues are difficult to overcome, this article assists in the training and development of women radiologists by offering strategies to enhance job satisfaction and bring new and valuable perspectives to leadership., (© Society of Breast Imaging 2024.)

In the rapidly evolving field of artificial intelligence (AI) for radiology, with a plethora of vendor options and use-cases and evidence claims to sift through, the pressing question is how to effectively implement the right tool for enhanced patient care? This article presents a structured approach to AI deployment, drawing from a comprehensive case study in South West London. We underscore the necessity of forming a dedicated AI team with a clear vision and assertive leadership to navigate such complexities. Central to our discussion is the significance of crafting an AI implementation plan, with an overarching aim to augment patient care, promote operational efficiency, and lay down standardized protocols for seamless AI adoption. By presenting a blueprint for AI implementation within the National Health Service (NHS), we intend to demystify the process for radiology departments across the UK, enabling them to make informed decisions and empowering their staff to embrace and leverage AI responsibly ensuring that patient welfare remains at the heart of innovation. Thus, having a framework to follow when implementing an AI solution that addresses a vision for scalable adoption, core team members with diversity of skillset, staff engagement and education, plan for vendor selection, and change management is crucial for success., (Copyright © 2024 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.)

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.

Artificial intelligence (AI) is a rapidly advancing technology in our society. The emergency radiology is an area facing an increase of the number of imaging studies and associated to the necessity to promptly deliver an accurate interpretation. The integration of AI algorithms to assist the clinician in providing analyses of the imaging studies while maintaining adequate diagnostic quality opens up new perspectives. There are numerous potential advantages of the implementation of AI in emergency radiology. However, the use of AI faces new challenges, as the algorithms reliability, data security, responsibility issues, and financial, human and material resources., Competing Interests: Les auteurs n’ont déclaré aucun conflit d’intérêts en relation avec cet article.

Low- and middle-income countries are significantly impacted by the global scarcity of medical imaging services. Medical imaging is an essential component for diagnosis and guided treatment, which is needed to meet the current challenges of increasing chronic diseases and preparedness for acute-care response. We present some key themes essential for improving global health equity, which were discussed at the 2023 RAD-AID Conference on International Radiology and Global Health. They include (1) capacity building, (2) artificial intelligence, (3) community-based patient navigation, (4) organizational design for multidisciplinary global health strategy, (5) implementation science, and (6) innovation. Although not exhaustive, these themes should be considered influential as we guide and expand global health radiology programs in low- and middle-income countries in the coming years., (Copyright © 2024 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Human factors engineering involves the study and development of methods aimed at enhancing performance, improving safety, and optimizing user satisfaction. The focus of human factors engineering encompasses the design of work environments and an understanding of human mental processes to prevent errors. In this review, we summarize the history, applications, and impacts of human factors engineering on the healthcare field. To illustrate these applications and impacts, we provide several examples of how successful integration of a human factors engineer in our pediatric radiology department has positively impacted various projects. The successful integration of human factors engineering expertise has contributed to projects including improving response times for portable radiography requests, deploying COVID-19 response resources, informing the redesign of scheduling workflows, and implementation of a virtual ergonomics program for remote workers. In sum, the integration of human factors engineering insight into our department has resulted in tangible benefits and has also positioned us as proactive contributors to broader hospital-wide improvements., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)