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Background: Artificial intelligence (AI) is increasingly finding its way into routine radiological work., Objective: Presentation of the current advances and applications of AI along the entire radiological patient journey., Methods: Systematic literature review of established AI techniques and current research projects, with reference to consensus recommendations., Results: The applications of AI in radiology cover a wide range, starting with AI-supported scheduling and indications assessment, extending to AI-enhanced image acquisition and reconstruction techniques that have the potential to reduce radiation doses in computed tomography (CT) or acquisition times in magnetic resonance imaging (MRI), while maintaining comparable image quality. These include computer-aided detection and diagnosis, such as fracture recognition or nodule detection. Additionally, methods such as worklist prioritization and structured reporting facilitated by large language models enable a rethinking of the reporting process. The use of AI promises to increase the efficiency of all steps of the radiology workflow and an improved diagnostic accuracy. To achieve this, seamless integration into technical workflows and proven evidence of AI systems are necessary., Conclusion: Applications of AI have the potential to profoundly influence the role of radiologists in the future., (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Background: Physicians have to make countless decisions every day. The medical, ethical and legal aspects are often intertwined and subject to change over time. Involving an ethics committee or arranging an ethical consultation are examples of potential aids to decision making. Whether and how artificial intelligence (AI) and the large language model (LLM) of the company OpenAI (San Francisco, CA, USA), known under the name ChatGPT, can also help and support ethical decision making is increasingly becoming a matter of controversial debate., Material and Methods: Based on a case example, in which a female physician is confronted with ethical and legal issues and presents these to ChatGPT to come up with answers, the first indications of the strengths and weaknesses are ascertained., Conclusion: Due to the rapid technical development and access to ever increasing quantities of data, the utilization should be closely observed and evaluated., (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Background: Rare diseases are often recognized late. Their diagnosis is particularly challenging due to the diversity, complexity and heterogeneity of clinical symptoms. Computer-aided diagnostic aids, often referred to as diagnostic decision support systems (DDSS), are promising tools for shortening the time to diagnosis. Despite initial positive evaluations, DDSS are not yet widely used, partly due to a lack of integration with existing clinical or practice information systems., Objective: This article provides an insight into currently existing diagnostic support systems that function without access to electronic patient records and only require information that is easily obtainable., Materials and Methods: A systematic literature search identified eight articles on DDSS that can assist in the diagnosis of rare diseases with no need for access to electronic patient records or other information systems in practices and hospitals. The main advantages and disadvantages of the identified rare disease diagnostic support systems were extracted and summarized., Results: Symptom checkers and DDSS based on portrait photos and pain drawings already exist. The degree of maturity of these applications varies., Conclusion: DDSS currently still face a number of challenges, such as concerns about data protection and accuracy, and acceptance and awareness continue to be rather low. On the other hand, there is great potential for faster diagnosis, especially for rare diseases, which are easily overlooked due to their large number and the low awareness of them. The use of DDSS should therefore be carefully considered by doctors on a case-by-case basis., (© 2023. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

We are in the middle of a digital revolution in medicine. This raises the question of whether subjects such as radiology, which is superficially concerned with the interpretation of images, will be particularly changed by this revolution. In particular, it should be discussed whether in the future the completion of initially simpler, then more complex image analysis tasks by computer systems may lead to a reduced need for radiologists. What distinguishes radiology in particular is its key position between advanced technology and medical care. This article discusses that not only radiology but every medical discipline will be affected by innovations due to the digital revolution, and that a redefinition of medical specialties focusing on imaging and visual interpretation makes sense and that the arrival of artificial intelligence (AI) in radiology is to be welcomed in the context of ever larger amounts of image data-to at all be able to handle the increasing amount of image data in the future at the current number of radiologists. In this respect, the balance between research and teaching in comparison to patient care is more difficult to maintain in the academic environment. AI can help improve efficiency and balance in the areas mentioned. With regard to specialist training, information technology topics are expected to be integrated into the radiological curriculum. Radiology acts as a pioneer designing the entry of AI into medicine. It is to be expected that by the time radiologists can be substantially replaced by AI, the replacement of human contributions in other medical and non-medical fields will also be well advanced., (© 2022. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Background: Physicians have to make countless decisions every day. The medical, ethical and legal aspects are often intertwined and subject to change over time. Involving an ethics committee or arranging an ethical consultation are examples of potential aids to decision making. Whether and how artificial intelligence (AI) and the large language model (LLM) of the company OpenAI (San Francisco, CA, USA), known under the name ChatGPT, can also help and support ethical decision making is increasingly becoming a matter of controversial debate., Material and Methods: Based on a case example, in which a female physician is confronted with ethical and legal issues and presents these to ChatGPT to come up with answers, the first indications of the strengths and weaknesses are ascertained., Conclusion: Due to the rapid technical development and access to ever increasing quantities of data, the utilization should be closely observed and evaluated., (© 2023. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Imaging instruments, such as conventional X‑ray, ultrasound and magnetic resonance imaging (MRI) are now fully established and highly valued in the care of rheumatology patients. However, the information provided by these imaging modalities in their current form is of limited utility for the prognostic prediction of individual patient outcomes. This article illuminates an important part of the development of imaging and shows that the vision of personalized medicine is becoming increasingly more tangible due to the further development of high-resolution imaging techniques, molecular imaging and artificial intelligence., (© 2023. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Background: With the introduction of ChatGPT in late November 2022, large language models based on artificial intelligence have gained worldwide recognition. These language models are trained on vast amounts of data, enabling them to process complex tasks in seconds and provide detailed, high-level text-based responses., Objective: To provide an overview of the most widely discussed large language models, ChatGPT and GPT‑4, with a focus on potential applications for patient-centered radiology., Materials and Methods: A PubMed search of both large language models was performed using the terms "ChatGPT" and "GPT-4", with subjective selection and completion in the form of a narrative review., Results: The generic nature of language models holds great promise for radiology, enabling both patients and referrers to facilitate understanding of radiological findings, overcome language barriers, and improve the quality of informed consent discussions. This could represent a significant step towards patient-centered or person-centered radiology., Conclusion: Large language models represent a promising tool for improving the communication of findings, interdisciplinary collaboration, and workflow in radiology. However, important privacy issues and the reliable applicability of these models in medicine remain to be addressed., (© 2023. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Big data and applications of artificial intelligence (AI), such as machine learning or deep learning, will enrich healthcare in the future and become increasingly important. Among other things, they have the potential to avoid unnecessary examinations as well as diagnostic and therapeutic errors. They could enable improved, early and accelerated decision-making. In the article, the authors provide an overview of current AI-based applications in cardiology. The examples describe innovative solutions for risk assessment, diagnosis and therapy support up to patient self-management. Big data and AI serve as a basis for efficient, predictive, preventive and personalised medicine. However, the examples also show that research is needed to further develop the solutions for the benefit of the patient and the medical profession, to demonstrate the effectiveness and benefits in health care and to establish legal and ethical standards.

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.)

Background: Interdisciplinary case discussions, especially tumor conferences, represent a large part of the clinical radiologist's daily work. Radiology plays a key role in tumor conferences, since imaging findings have a direct influence on therapy decisions., Methods and Objectives: This article discusses the requirements for the radiologist in preparing and conducting tumor conferences. Furthermore, the general conditions and forms of implementation of tumor conferences will be highlighted. Information technology (IT) tools for process automation and systems for assessing the course of tumor diseases will be presented., Results: Detailed preparation of tumor conferences and clear communication of findings is essential. The radiological expertise in tumor conferences often leads to changes or adjustments of initially planned therapies. In addition to traditional face-to-face meetings, hybrid solutions have become established for tumor conferences in which the core team is on site and other participants (external referring physicians, internal participants outside the core team) are connected via video conference. Various systems have been established for assessing the course of tumor diseases. Due to its broad applicability, RECIST 1.1. is the most widely used. IT tools enable previously marked lesions to be displayed over time in a matrix view (lesion tracking). Artificial intelligence (AI) can also be used to automatically detect lesions and assess their volumes., Conclusion: Preparing and conducting tumor conferences is time-consuming for radiologists. IT tools can automate and thus facilitate the processes. Hybrid solutions combining face-to-face meetings and video conferences make it easier for external referring physicians to present their patients in tumor conferences., (© 2023. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Clinical/methodological Issue: Central to breast imaging is the coordination of clinical and multimodal imaging information with percutaneous image-guided biopsies and surgical procedures. A wide range of problems arise due to this complexity: missed cancers, overdiagnosis, false-positive findings, unnecessary further imaging, biopsies and surgeries., Standard Radiological Methods: Breast imaging comprises the following diagnostic tests: mammography, tomosynthesis, contrast-enhanced mammography, (multiparametric) ultrasound, magnetic resonance imaging, computed tomography, nuclear medicine derived imaging and hybrid methods., Methodological Innovations: Artificial intelligence (AI) promises to alleviate practically all these problems of breast imaging. AI has the potential to avoid missed cancers and false-positive findings. Furthermore, it could guide an efficient use of imaging methods and it may potentially be used to define biological phenotypes of breast cancer., Performance: AI-based software is being developed for various applications. Most developed are systems that support mammography screening. Problems are monocentric approaches and the focus on short-term financial success., Achievements: AI promises to improve breast imaging by simplifying and speeding up the workflow, by reducing monotonous tasks and by pointing out problems. This is likely to set free physician capacities that could be invested in improved communication with patients and interdisciplinary colleagues., Practical Recommendations: The present article mainly addresses clinical needs in breast imaging, pointing out potential areas of use for artificial intelligence. Depending on the definition, a wide array of helpful software tools for breast imaging are already available. Global solutions, however, are still missing.

Background: Artificial intelligence (AI) has the potential to fundamentally change medicine within the coming decades. Radiological imaging is one of the primary fields of its clinical application., Objectives: In this article, we summarize previous AI developments with a focus on oncological radiology. Based on selected examples, we derive scenarios for developments in the next 10 years., Materials and Methods: This work is based on a review of various literature and product databases, publications by regulatory authorities, reports, and press releases., Conclusions: The clinical use of AI applications is still in an early stage of development. The large number of research publications shows the potential of the field. Several certified products have already become available to users. However, for a widespread adoption of AI applications in clinical routine, several fundamental prerequisites are still awaited. These include the generation of evidence justifying the use of algorithms through representative clinical studies, adjustments to the framework for approval processes and dedicated education and teaching resources for its users. It is expected that use of AI methods will increase, thus, creating new opportunities for improved diagnostics, therapy, and more efficient workflows.

Background: In 2023, the release of ChatGPT triggered an artificial intelligence (AI) boom. The underlying large language models (LLM) of the nonprofit organization "OpenAI" are not freely available under open-source licenses, which does not allow on-site implementation inside secure clinic networks. However, efforts are being made by open-source communities, start-ups and large tech companies to democratize the use of LLMs. This opens up the possibility of using LLMs in a data protection-compliant manner and even adapting them to our own data., Objectives: This paper aims to explain the potential of privacy-compliant local LLMs for radiology and to provide insights into the "open" versus "closed" dynamics of the currently rapidly developing field of AI., Materials and Methods: PubMed search for radiology articles with LLMs and subjective selection of references in the sense of a narrative key topic article., Results: Various stakeholders, including large tech companies such as Meta, Google and X, but also European start-ups such as Mistral AI, contribute to the democratization of LLMs by publishing the models (open weights) or by publishing the model and source code (open source). Their performance is lower than current "closed" LLMs, such as GPT‑4 from OpenAI., Conclusion: Despite differences in performance, open and thus locally implementable LLMs show great promise for improving the efficiency and quality of diagnostic reporting as well as interaction with patients and enable retrospective extraction of diagnostic information for secondary use of clinical free-text databases for research, teaching or clinical application., (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

The use of artificial intelligence (AI) in healthcare has made significant progress in the last 10 years. Many experts believe that utilization of AI technologies, especially deep learning, will bring about drastic changes in how physicians understand, diagnose, and treat diseases. One aspect of this development is AI-enhanced electrocardiography (ECG) analysis. It involves not only optimizing the traditional ECG analysis by the physician and improving the accuracy of automatic interpretation by the ECG device but also introducing entirely new diagnostic options enabled by AI. Examples include assessing left ventricular function, predicting atrial fibrillation, and diagnosing both cardiac and noncardiac conditions. Through AI, the ECG becomes a comprehensive tool for screening, diagnosis, and patient management, potentially revolutionizing clinical practices. This paper provides an overview of the current state of this development, discusses existing limitations, and explores the challenges that may arise for healthcare professionals in this context., (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

With the advancements in precision medicine, the demands on pathological diagnostics have increased, requiring standardized, quantitative, and integrated assessments of histomorphological and molecular pathological data. Great hopes are placed in artificial intelligence (AI) methods, which have demonstrated the ability to analyze complex clinical, histological, and molecular data for disease classification, biomarker quantification, and prognosis estimation. This paper provides an overview of the latest developments in pathology AI, discusses the limitations, particularly concerning the black box character of AI, and describes solutions to make decision processes more transparent using methods of so-called explainable AI (XAI)., (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

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This paper examines the problem of hallucinations in four scientific articles generated by ChatGPT-4 from various sub-fields of business administration. Specifically, the aim is to check whether the references generated by ChatGPT exist and, if this is the case, whether they are correct. A first surprising result is that nearly 40 Prozent of the 174 sources referenced in the articles, whose bibliographic details were listed after the individual main chapters, were not included in the specially for each article generated bibliography. A check of the bibliographic entries revealed that between 18 % and 100 % of the sources in the individual articles could not be found with Google Scholar. Furthermore, half of the sources found were incorrect to varying degrees (wrong document type, wrong journal name, wrong page reference). On average, only a quarter of the bibliographic references were completely correct. In Web of Science, only a quarter of the sources could be verified, but most of them were correct. The above results show that ChatGPT-4 – besides ethical and legal concerns – should definitely not be used for writing scientific papers, as a large proportion of the references generated by ChatGPT (valid at the time of writing this paper) does not exist or is partially incorrect. Furthermore, it can be assumed that some of the article texts generated are also "invented" or come from unreferenced sources (plagiarism). Texts generated by ChatGPT must therefore be subject to a comprehensive review, which may not always be easy. The authors therefore agree with the recommendation of Gimpel et al. (2023) that ChatGPT should primarily be used as a supplement to writing (e. g., spell-checking, creating summaries, supporting the generation of ideas). [ABSTRACT FROM AUTHOR]

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Why do many social scientists use convoluted, bloated phrases that render their papers incomprehensible? This essay traces the urge to indulge in pompous ramblings by analysing the author's own undergraduate essays in political science. There, the impenetrable diction hides an inability to grasp the subject. Stylistic blunders, such as mixed metaphors and a recurring use of the passive, not only make for a bad read; they often distort the issue. Surprisingly, in this respect the author's early attempts at academic writing have a lot in common with more established works. But what is the link between social science and bad language? This essay examines the connection between awkward language and academic research conditions in the context of two recent developments: growing pressure to measure the quality of academic writing in quantitative terms, and a creeping elimination of unregimented reading experience. [ABSTRACT FROM AUTHOR]

The increasing digitization of the healthcare system is leading to a growing volume of health data. Leveraging this data beyond its initial collection purpose for secondary use can provide valuable insights into diagnostics, treatment processes, and the quality of care. The Health Data Lab (HDL) will provide infrastructure for this purpose. Both the protection of patient privacy and optimal analytical capabilities are of central importance in this context, and artificial intelligence (AI) provides two opportunities. First, it enables the analysis of large volumes of data with flexible models, which means that hidden correlations and patterns can be discovered. Second, synthetic - that is, artificial - data generated by AI can protect privacy.This paper describes the KI-FDZ project, which aims to investigate innovative technologies that can support the secure provision of health data for secondary research purposes. A multi-layered approach is investigated in which data-level measures can be combined in different ways with processing in secure environments. To this end, anonymization and synthetization methods, among others, are evaluated based on two concrete application examples. Moreover, it is examined how the creation of machine learning pipelines and the execution of AI algorithms can be supported in secure processing environments. Preliminary results indicate that this approach can achieve a high level of protection while maintaining data validity. The approach investigated in the project can be an important building block in the secure secondary use of health data., (© 2024. The Author(s).)

Background: Artificial intelligence (AI) is becoming increasingly important for the future development of hospitals. To unlock the large potential of AI, job profiles of hospital staff members need to be further developed in the direction of AI and digitization skills through targeted qualification measures. This affects both medical and non-medical processes along the entire value chain in hospitals. The aim of this paper is to provide an overview of the skills required to deal with smart technologies in a clinical context and to present measures for training employees., Methods: As part of the "SmartHospital.NRW" project in 2022, we conducted a literature review as well as interviews and workshops with experts. AI technologies and fields of application were identified., Results: Key findings include adapted and new task profiles, synergies and dependencies between individual task profiles, and the need for a comprehensive interdisciplinary and interprofessional exchange when using AI-based applications in hospitals., Discussion: Our article shows that hospitals need to promote digital health literacy skills for hospital staff members at an early stage and at the same time recruit technology- and AI-savvy staff. Interprofessional exchange formats and accompanying change management are essential for the use of AI in hospitals., (© 2023. The Author(s).)

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Die Öffnung ihrer digitalen Sammlungen für die Nachnutzung ist ein wichtiges strategisches Ziel der Österreichischen Nationalbibliothek. Der vorliegende Beitrag beschäftigt sich mit Fragen der Digitalisierung und der Öffnung digitaler Sammlungen und diskutiert, wie diese am besten als Daten für die Forschung zugänglich gemacht werden können. Ein Schwerpunkt liegt dabei auf historischen Zeitungen im Forschungskontext. Weiters wird auf das Konzept von „Collections as Data" und die Rolle von GLAM Labs eingegangen, die in jüngster Zeit in vielen Bibliotheken, darunter auch in der Österreichischen Nationalbibliothek, eingerichtet wurden. Opening its digital collections for reuse is an important strategic goal of the Austrian National Library. This paper deals with questions of digitisation and opening digital collections and discusses how these can best be made accessible as data for research. One focus is on historical newspapers in a research context. This paper also discusses the concept of "Collections as Data" and the role of GLAM Labs, which have recently been established in many libraries, including the Austrian National Library. [ABSTRACT FROM AUTHOR]

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Artificial intelligence (AI) opens up new opportunities to improve medical care in internal medicine; however, legal uncertainties in the application of AI impede its integration into the daily practice of internal medicine. To clarify the situation this paper gives an overview of the legal aspects related to AI and shows which frameworks must be adhered to in order to exploit the benefits of AI without neglecting the rights and protection of patients. The paper first addresses data protection issues which arise when sensitive health data are processed by AI. This is followed by a discussion of the key regulatory requirements for the use of AI in internal medicine. As the establishment of AI in practice also depends on sufficient funding, legal issues of reimbursement are additionally examined. Finally, the specific features that need to be considered when using AI to avoid medical liability consequences are highlighted., (© 2023. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Background: Artificial intelligence is playing an increasingly important role in radiology. However, more and more often it is no longer possible to reconstruct decisions, especially in the case of new and powerful methods from the field of deep learning. The resulting models fulfill their function without the users being able to understand the internal processes and are used as so-called black boxes. Especially in sensitive areas such as medicine, the explainability of decisions is of paramount importance in order to verify their correctness and to be able to evaluate alternatives. For this reason, there is active research going on to elucidate these black boxes., Method: This review paper presents different approaches for explainable artificial intelligence with their advantages and disadvantages. Examples are used to illustrate the introduced methods. This study is intended to enable the reader to better assess the limitations of the corresponding explanations when meeting them in practice and strengthen the integration of such solutions in new research projects., Results and Conclusion: Besides methods to analyze black-box models for explainability, interpretable models offer an interesting alternative. Here, explainability is part of the process and the learned model knowledge can be verified with expert knowledge., Key Points: · The use of artificial intelligence in radiology offers many possibilities to provide safer and more efficient medical care. This includes, but is not limited to support during image acquisition and processing or for diagnosis.. · Complex models can achieve high accuracy, but make it difficult to understand data processing.. · If the explainability is already taken into account during the planning of the model, methods can be developed that are powerful and interpretable at the same time.., Citation Format: · Gallée L, Kniesel H, Ropinski T et al. Artificial intelligence in radiology - beyond the black box. Fortschr Röntgenstr 2023; 195: 797 - 803., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

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