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Artificial intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. KEY POINTS., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

The introduction of artificial intelligence (AI) into radiology promises to enhance efficiency and improve diagnostic accuracy, yet it also raises manifold ethical questions. These include data protection issues, the future role of radiologists, liability when using AI systems, and the avoidance of bias. To prevent data bias, the datasets need to be compiled carefully and to be representative of the target population. Accordingly, the upcoming European Union AI act sets particularly high requirements for the datasets used in training medical AI systems. Cognitive bias occurs when radiologists place too much trust in the results provided by AI systems (overreliance). So far, diagnostic AI systems are used almost exclusively as "second look" systems. If diagnostic AI systems are to be used in the future as "first look" systems or even as autonomous AI systems in order to enhance efficiency in radiology, the question of liability needs to be addressed, comparable to liability for autonomous driving. Such use of AI would also significantly change the role of radiologists., (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Background:  In recent years, AI has made significant advancements in medical diagnosis and prognosis. However, the incorporation of AI into clinical practice is still challenging and under-appreciated. We aim to demonstrate a possible vertical integration approach to close the loop for AI-ready radiology., Method:  This study highlights the importance of two-way communication for AI-assisted radiology. As a key part of the methodology, it demonstrates the integration of AI systems into clinical practice with structured reports and AI visualization, giving more insight into the AI system. By integrating cooperative lifelong learning into the AI system, we ensure the long-term effectiveness of the AI system, while keeping the radiologist in the loop.  RESULTS:  We demonstrate the use of lifelong learning for AI systems by incorporating AI visualization and structured reports. We evaluate Memory Aware-Synapses and Rehearsal approach and find that both approaches work in practice. Furthermore, we see the advantage of lifelong learning algorithms that do not require the storing or maintaining of samples from previous datasets., Conclusion:  In conclusion, incorporating AI into the clinical routine of radiology requires a two-way communication approach and seamless integration of the AI system, which we achieve with structured reports and visualization of the insight gained by the model. Closing the loop for radiology leads to successful integration, enabling lifelong learning for the AI system, which is crucial for sustainable long-term performance., Key Points:   · The integration of AI systems into the clinical routine with structured reports and AI visualization.. · Two-way communication between AI and radiologists is necessary to enable AI that keeps the radiologist in the loop.. · Closing the loop enables lifelong learning, which is crucial for long-term, high-performing AI in radiology.., Competing Interests: The authors of this manuscript declare relationships with the following companies: Phillip Matthies is an Employee of Smart Reporting GmbH., (Thieme. All rights reserved.)

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. This article is simultaneously published in Insights into Imaging (DOI 10.1186/s13244-023-01541-3), Journal of Medical Imaging and Radiation Oncology (DOI 10.1111/1754-9485.13612), Canadian Association of Radiologists Journal (DOI 10.1177/08465371231222229), Journal of the American College of Radiology (DOI 10.1016/j.jacr.2023.12.005), and Radiology: Artificial Intelligence (DOI 10.1148/ryai.230513). Artificial Intelligence, Radiology, Automation, Machine Learning Published under a CC BY 4.0 license. ©The Author(s) 2024. Editor's Note: The RSNA Board of Directors has endorsed this article. It has not undergone review or editing by this journal.Keywords: Artificial Intelligence, Radiology, Automation, Machine Learning Published under a CC BY 4.0 license. ©The Author(s) 2024. Editor's Note: The RSNA Board of Directors has endorsed this article. It has not undergone review or editing by this journal.

Background: The hype around artificial intelligence (AI) in radiology continues and the number of approved AI tools is growing steadily. Despite the great potential, integration into clinical routine in radiology remains limited. In addition, the large number of individual applications poses a challenge for clinical routine, as individual applications have to be selected for different questions and organ systems, which increases the complexity and time required., Objectives: This review will discuss the current status of validation and implementation of AI tools in clinical routine, and identify possible approaches for an improved assessment of the generalizability of results of AI tools., Materials and Methods: A literature search in various literature and product databases as well as publications, position papers, and reports from various stakeholders was conducted for this review., Results: Scientific evidence and independent validation studies are available for only a few commercial AI tools and the generalizability of the results often remains questionable., Conclusions: One challenge is the multitude of offerings for individual, specific application areas by a large number of manufacturers, making integration into the existing site-specific IT infrastructure more difficult. Furthermore, remuneration for the use of AI tools in clinical routine by health insurance companies in Germany is lacking. But in order for reimbursement to be granted, the clinical utility of new applications must first be proven. Such proof, however, is lacking for most applications., (© 2022. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

There has been an exponential rise in artificial intelligence (AI) research in imaging in recent years. While the dissemination of study data that has the potential to improve clinical practice is welcomed, the level of detail included in early AI research reporting has been highly variable and inconsistent, particularly when compared to more traditional clinical research. However, inclusion checklists are now commonly available and accessible to those writing or reviewing clinical research papers. AI-specific reporting guidelines also exist and include distinct requirements, but these can be daunting for radiologists new to the field. Given that pediatric radiology is a specialty faced with workforce shortages and an ever-increasing workload, AI could help by offering solutions to time-consuming tasks, thereby improving workflow efficiency and democratizing access to specialist opinion. As a result, pediatric radiologists are expected to be increasingly leading and contributing to AI imaging research, and researchers and clinicians alike should feel confident that the findings reported are presented in a transparent way, with sufficient detail to understand how they apply to wider clinical practice. In this review, we describe two of the most clinically relevant and available reporting guidelines to help increase awareness and engage the pediatric radiologist in conducting AI imaging research. This guide should also be useful for those reading and reviewing AI imaging research and as a checklist with examples of what to expect., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objectives: To find out the opinion of radiological inpatient and outpatient medical staff regarding the measures taken in relation to the COVID-19 pandemic during the first and second waves and to identify the measures that are still perceived as needing improvement., Materials and Methods: We conducted an anonymous online survey among more than 10 000 radiologists/technicians in Germany from January 5 to January 31, 2021. A total of 862 responses (head physicians, n = 225 [inpatient doctors, n = 138; outpatient doctors, n = 84; N/A, n = 3]; radiologic personnel, n = 637 [inpatient doctor, n = 303; outpatient doctor, n = 50; inpatient technician, n = 217; outpatient technician, n = 26; N/A, n = 41]) were received. Questions of approximation, yes/no questions, and Likert scales were used., Results: During the first/second wave, 70 % (86/123)/43 % (45/104) of inpatient and 26 % (17/66)/10 % (5/52) of outpatient head physicians agreed that they received financial support from the authorities but the majority rated the financial support as insufficient. During the first and second wave, 33 % (8/24) and 80 % (16/20) of outpatient technicians agreed that they were adequately provided with personal protective equipment. The perceived lack of personal protective equipment improved for all participants during the second wave. Inpatient [outpatient] technicians perceived an increased workload in the first and second wave: 72 % (142/198) [79 % (19/24)] and 84 % (146/174) [80 % (16/20)]., Conclusion: Technicians seem increasingly negatively affected by the COVID-19 pandemic in Germany. Financial support by the competent authorities seems to be in need of improvement., Key Points: · The accessibility of personal protective equipment resources improved in the second wave.. · In particular, radiology technicians seem increasingly negatively affected by the COVID-19 pandemic.. · Financial and consulting support from the government could be improved.., Citation Format: · Bernatz S, Afat S, Othman AE et al. Impact of the COVID-19 Pandemic on Radiology in Inpatient and Outpatient Care in Germany: A Nationwide Survey Regarding the First and Second Wave. Fortschr Röntgenstr 2022; 194: 70 - 82., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

Background: There are a multitude of application possibilities of artificial intelligence (AI) and structured reporting (SR) in radiology. The number of scientific publications have continuously increased for many years. There is an extensive portfolio of available AI algorithms for, e.g. automatic detection and preselection of pathologic patterns in images or for facilitating the reporting workflows. Even machines already use AI algorithms for improvement of operating comfort., Method: The use of SR is essential especially for the extraction of automatically evaluable semantic data from radiology results reports. Regarding eligibility in certification processes, the use of SR is mandatory for the accreditation of the German Cancer Society as an oncological center or outside Germany, such as the European Cancer Center., Results: The data from SR can be automatically evaluated for the purpose of patient care, research and educational purposes and quality assurance. Lack of information and a high degree of variability often hamper the extraction of valid information from free-text reports using neurolinguistic programming (NLP). Against the background of supervised training, AI algorithms or k‑nearest neighbors (KNN) require a considerable amount of validated data. The semantic data from SR can also be processed by AI and used for training., Conclusion: The AI and SR are separate entities within the field of radiology with mutual dependencies and significant added value. Both have a high potential for profound upcoming changes and further developments in radiology., (© 2021. Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Numerous publications have shown the value of structured reporting in communication with referring physicians and for further usage of clinical report data in other contexts. Despite the topic being present and known in radiology for many years now, widespread adoption of structured reporting in clinical routine is still lacking. All major radiological societies have published position statements in favor of structured reporting and are pursuing various related initiatives. Among those are the development and the maintenance of openly available structured report template repositories as well as quality control of report templates and development of standardized terminologies. This article highlights the efforts of the German Radiological Society and the European Society of Radiology on the topic of structured reporting and provides an overview of pros and cons as well as publicly available resources., (© 2021. Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Artificial intelligence (AI) has made impressive progress over the past few years, including many applications in medical imaging. Numerous commercial solutions based on AI techniques are now available for sale, forcing radiology practices to learn how to properly assess these tools. While several guidelines describing good practices for conducting and reporting AI-based research in medicine and radiology have been published, fewer efforts have focused on recommendations addressing the key questions to consider when critically assessing AI solutions before purchase. Commercial AI solutions are typically complicated software products, for the evaluation of which many factors are to be considered. In this work, authors from academia and industry have joined efforts to propose a practical framework that will help stakeholders evaluate commercial AI solutions in radiology (the ECLAIR guidelines) and reach an informed decision. Topics to consider in the evaluation include the relevance of the solution from the point of view of each stakeholder, issues regarding performance and validation, usability and integration, regulatory and legal aspects, and financial and support services. KEY POINTS: • Numerous commercial solutions based on artificial intelligence techniques are now available for sale, and radiology practices have to learn how to properly assess these tools. • We propose a framework focusing on practical points to consider when assessing an AI solution in medical imaging, allowing all stakeholders to conduct relevant discussions with manufacturers and reach an informed decision as to whether to purchase an AI commercial solution for imaging applications. • Topics to consider in the evaluation include the relevance of the solution from the point of view of each stakeholder, issues regarding performance and validation, usability and integration, regulatory and legal aspects, and financial and support services.

Objectives: We investigated the attitudes of radiologists, information technology (IT) specialists, and industry representatives on artificial intelligence (AI) and its future impact on radiological work., Materials and Methods: During a national meeting for AI, eHealth, and IT infrastructure in 2019, we conducted a survey to obtain participants' attitudes. A total of 123 participants completed 28 items exploring AI usage in medicine. The Kruskal-Wallis test was used to identify differences between radiologists, IT specialists, and industry representatives., Results: The strongest agreement between all respondents occurred with the following: plausibility checks are important to understand the decisions of the AI (93% agreement), validation of AI algorithms is mandatory (91%), and medicine becomes more efficient in the age of AI (86%). In contrast, only 25% of the respondents had confidence in the AI results, and only 17% believed that medicine will become more human through the use of AI. The answers were significantly different between the three professions for four items: relevance for protocol selection in cross-sectional imaging (p = 0.034), medical societies should be involved in validation (p = 0.028), patients should be informed about the use of AI (p = 0.047), and AI should be part of medical education (p = 0.026)., Conclusion: Currently, a discrepancy exists between high expectations for the future role of AI and low confidence in the results. This attitude was similar across all three groups. The demand for plausibility checks and the need to prove the usefulness in randomized controlled studies indicate what is needed in future research., Competing Interests: Conflict of interest The authors of this paper declare no relationships with any companies whose products or services may be related to the subject matter of the article., (Copyright © 2020 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

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Summary: Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever‐growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi‐society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. [ABSTRACT FROM AUTHOR]

Structured reporting in radiology continues to hold substantial potential to improve the quality of service provided to patients and referring physicians. Despite many physicians' preference for structured reports and various efforts by radiological societies and some vendors, structured reporting has still not been widely adopted in clinical routine. While in many countries national radiological societies have launched initiatives to further promote structured reporting, cross-institutional applications of report templates and incentives for usage of structured reporting are lacking. Various legislative measures have been taken in the USA and the European Union to promote interoperable data formats such as Fast Healthcare Interoperability Resources (FHIR) in the context of the EU Health Data Space (EHDS) which will certainly be relevant for the future of structured reporting. Lastly, recent advances in artificial intelligence and large language models may provide innovative and efficient approaches to integrate structured reporting more seamlessly into the radiologists' workflow. The ESR will remain committed to advancing structured reporting as a key component towards more value-based radiology. Practical solutions for structured reporting need to be provided by vendors. Policy makers should incentivize the usage of structured radiological reporting, especially in cross-institutional setting. Critical relevance statement Over the past years, the benefits of structured reporting in radiology have been widely discussed and agreed upon; however, implementation in clinical routine is lacking due—policy makers should incentivize the usage of structured radiological reporting, especially in cross-institutional setting. Key points 1. Various national societies have established initiatives for structured reporting in radiology. 2. Almost no monetary or structural incentives exist that favor structured reporting. 3. A consensus on technical standards for structured reporting is still missing. 4. The application of large language models may help structuring radiological reports. 5. Policy makers should incentivize the usage of structured radiological reporting. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the epidemiology and distribution of disease characteristics of urolithiasis by data mining structured radiology reports. Methods: The content of structured radiology reports of 2028 urolithiasis CTs was extracted from the department's structured reporting (SR) platform. The investigated cohort represented the full spectrum of a tertiary care center, including mostly symptomatic outpatients as well as inpatients. The prevalences of urolithiasis in general and of nephro- and ureterolithasis were calculated. The distributions of age, sex, calculus size, density and location, and the number of ureteral and renal calculi were calculated. For ureterolithiasis, the impact of calculus characteristics on the degree of possible obstructive uropathy was calculated. Results: The prevalence of urolithiasis in the investigated cohort was 72%. Of those patients, 25% had nephrolithiasis, 40% ureterolithiasis, and 35% combined nephro- and ureterolithiasis. The sex distribution was 2.3:1 (M:F). The median patient age was 50 years (IQR 36–62). The median number of calculi per patient was 1. The median size of calculi was 4 mm, and the median density was 734 HU. Of the patients who suffered from ureterolithiasis, 81% showed obstructive uropathy, with 2nd-degree uropathy being the most common. Calculus characteristics showed no impact on the degree of obstructive uropathy. Conclusion: SR-based data mining is a simple method by which to obtain epidemiologic data and distributions of disease characteristics, for the investigated cohort of urolithiasis patients. The added information can be useful for multiple purposes, such as clinical quality assurance, radiation protection, and scientific or economic investigations. To benefit from these, the consistent use of SR is mandatory. However, in clinical routine SR usage can be elaborate and requires radiologists to adapt. [ABSTRACT FROM AUTHOR]

In 2013, the new European Basic Safety Standards Directive 2013/59/Euratom (BSS Directive), which defines the new legal framework for the use of ionising radiation in medical imaging and radiotherapy, was published. In 2014, the ESR EuroSafe Imaging Initiative was founded with a goal in mind "to support and strengthen medical radiation protection across Europe following a holistic, inclusive approach". To support radiology departments in developing a programme of clinical audit, the ESR developed a Guide to Clinical Audit and an accompanying audit tool in 2017, with an expanded second edition released in 2019 and published under the name of Esperanto – ESR Guide to Clinical Audit in Radiology and the ESR Clinical Audit Tool, 2019. Audits represent specific aspects at a certain point in time, usually with retrospective evaluation of data. Key performance indicators (KPIs), on the other hand, are intended to enable continuous monitoring of relevant parameters, for example to provide warnings or a dashboard. KPIs, which can, for example, be recorded automatically and visualised in dashboards, are suitable for this purpose. This paper will discuss a selection of indicators covering different areas and include suggestions for their implementation. [ABSTRACT FROM AUTHOR]

Objectives: Interventional radiology (IR) is a growing field but is underrepresented in most medical school curricula. We tested whether endovascular simulator training improves medical students' attitudes towards IR.Materials and Methods: We conducted this prospective study at two university medical centers; overall, 305 fourth-year medical students completed a 90-min IR course. The class consisted of theoretical and practical parts involving endovascular simulators. Students completed questionnaires before the course, after the theoretical and after the practical part. On a 7-point Likert scale, they rated their interest in IR, knowledge of IR, attractiveness of IR, and the likelihood to choose IR as subspecialty. We used a crossover design to prevent position-effect bias.Results: The seminar/simulator parts led to the improvement for all items compared with baseline: interest in IR (pre-course 5.2 vs. post-seminar/post-simulator 5.5/5.7), knowledge of IR (pre-course 2.7 vs. post-seminar/post-simulator 5.1/5.4), attractiveness of IR (pre-course 4.6 vs. post-seminar/post-simulator 4.8/5.0), and the likelihood of choosing IR as a subspecialty (pre-course 3.3 vs. post-seminar/post-simulator 3.8/4.1). Effect was significantly stronger for simulator training compared with that for seminar for all items (p < 0.05). For simulator training, subgroup analysis of students with pre-existing positive attitude showed considerable improvement regarding "interest in IR" (× 1.4), "knowledge of IR" (× 23), "attractiveness of IR" (× 2), and "likelihood to choose IR" (× 3.2) compared with pretest.Conclusion: Endovascular simulator training significantly improves students' attitude towards IR regarding all items. Implementing such courses at a very early stage in the curriculum should be the first step to expose medical students to IR and push for IR.Key Points: • Dedicated IR-courses have a significant positive effect on students' attitudes towards IR. • Simulator training is superior to a theoretical seminar in positively influencing students' attitudes towards IR. • Implementing dedicated IR courses in medical school might ease recruitment problems in the field. [ABSTRACT FROM AUTHOR]

Objectives: Podcasts are audio recordings distributed via the Internet. We review the availability of podcasts on the topic of radiology. Methods: A search for podcasts relating to radiology was performed using search engines and free public websites that either hosted or distributed podcasts. Only English language podcast series were included, and video podcasts were excluded. Data was gathered by manually interrogating the metadata on the primary hosting platform and related websites. Results: Forty-one podcast series met the inclusion criteria. The earliest was from 2005. In total, 56.1% of podcasts were defined as active and 43.9% inactive at the time of publication. Number of episodes for each podcast series ranged from 1 to 269 with 56.1% of podcasts having ≤ 10 episodes. There was a wide variation in podcast series' frequency/schedules. The most common subject topic was 'radiology current affairs' (43.9%), with the least common 'exam revision' (7.3%) and 'radiography' (7.3%). The majority of podcasts were targeted at radiologists (87.8%) and originated from the USA (70.1%). Podcast hosts consisted of doctors (63.4%), other professionals (29.3%) or unknown (7.3%). Additional supplementary media or information as show notes were provided by 26.8% of radiology podcast series. Conclusions: This gives a new insight into the world of 'radiology podcasting'. To the authors' knowledge, this is the first review in the literature and highlights the increasing availability of podcasting in radiology. [ABSTRACT FROM AUTHOR]

This paper studies the possibilities of an integrated IT-based workflow for epidemiological research in pulmonary embolism (PE) using freely available tools and structured reporting (SR). We included a total of 521 consecutive cases which had been referred to the radiology department for CT pulmonary angiography with suspected PE. Free-text reports were transformed into structured reports using a freely available IHE Management of Radiology Report Templates-compliant reporting platform. D-dimer values were retrieved from the hospitals laboratory results system. All information was stored in the platform's database and visualized using freely available tools. For further analysis, we directly accessed the platform's database with an advanced analytics tool (RapidMiner). Results: We were able to develop an integrated workflow for epidemiological statistics from reports obtained in clinical routine. The report data allowed for automated calculation of epidemiological parameters. Prevalence of PE was 27.6%. The mean age in patients with and without PE did not differ (62.8 years and 62.0 years, respectively, p = 0.987). As expected, there was a significant difference in mean D-dimer values (10.13 and 3.12 mg l−1 fibrinogen equivalent units, respectively, p < 0.001). Conclusion: SR can make data obtained from clinical routine more accessible. Designing practical workflows is feasible using freely available tools and allows for the calculation of epidemiological statistics on a near realtime basis. Therefore, radiologists should push for the implementation of SR in clinical routine. Implementing practical workflows that allow for the calculation of epidemiological statistics using SR and freely available tools is easily feasible. Theoretical benefits of SR have long been discussed, but practical implementation demonstrating those benefits has been lacking. Here, we present a first experience providing proof that SR will make data from clinical routine more accessible. [ABSTRACT FROM AUTHOR]