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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.)

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.)

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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Copyright of Unterrichtswissenschaft (Springer Science & Business Media B.V.) is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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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The German Society for Information and Knowledge (DGI) invites its members to the regular general meeting on November 8, 2024. At the meeting, a new board will be elected and the annual budget plan will be discussed and approved. DGI is also organizing a theme day where the working groups AKTS and IuG will present their work results and seek exchange with business, science, and administration. Training and further education opportunities on the use of Artificial Intelligence (AI) will also be offered. DGI mourns the loss of long-time member Dr. Robert Fugmann, who passed away at the age of 97. The German Society for Information Science and Information Practice (DGI) offers various services related to legal aspects, knowledge management, and information literacy. In 2023, the focus of the Information Literacy and Education (IKB) working group was on the anniversary symposium of DGI. Discussions on topics such as fake news and information literacy took place. DGI also plans to update a position paper on the topic of Artificial Intelligence and Education. Events to promote information literacy were held, and collaboration with other organizations was maintained. The FaMI conference in 2024 will be hosted by the Joseph-DuMont Vocational College in Cologne. Approximately 30 vocational school teachers for media and information services met for professional exchange and further education. The program included topics such as project work and podcast production. Excursions to various institutions provided insights into the practice and possible work environments of the apprentices. It was emphasized that the field of information and documentation services needs to be strengthened nationwide. [Extracted from the article]

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Big data and artificial intelligence are buzzwords that everyone is talking about and yet always provide a touch of science fiction to the scenery. What is the status of these topics in anesthesia? Are the first robots already rolling through the corridors while doctors are getting bored as all the work has been done? Spoiler alert! We are still far away from achieving this. Initially, paper charts and analogue notes stand in the way of comprehensive digitization. Source systems need to be merged and data standardized, harmonized and validated. Therefore, the friendly android that is rolling towards us, waving and holding a freshly brewed cup of coffee in our thoughts will have to wait; however, a glimpse of the future is already evident in some clinics and the first promising developments are already showing what could be the standard tomorrow. Learning algorithms calculate the length of stay individually for each patient in the intensive care unit (ICU), reducing negative consequences such as readmission and mortality. The field of ultrasound technology for regional anesthesia and closed-loop anesthesia systems is also demonstrating the benefits of artificial intelligence (AI)-assisted technologies in practice. The efforts are diverse and ambitious but they repeatedly collide with privacy challenges and significant capital expenditure, which weigh heavily on an already financially strained healthcare system; however, anyone who listens carefully to the medical staff knows that robots are not what they would expect and the buzzwords big data and artificial intelligence might be less science fiction than initially assumed. [ABSTRACT FROM AUTHOR]

This paper considers the transfer of approaches from the field of to the field of to be used in decision support systems. The most relevant of the developed approaches will be roughly introduced and it will be considered how these approaches can be applied in medical informatics in the context of decision support systems. Moreover, it will be considered which of the approaches are eligible in this context. Apart from placing the approaches in the context of decision support systems, the paper thereby also provides an example for the interplay of different fields of research.

Artificial intelligence (AI) and robotics have emerged as game-changing technologies with the potential to revolutionize the healthcare industry. In the context of hospitals, their integration holds the promise of not only improving patient care but also driving competitive advantage and fostering sustainable development. This review paper aims to explore and evaluate the impact of AI and robotics applications on attaining competitive advantage and promoting sustainable developmenospitals, examines the current landscape of AI and robotics adoption in healthcare settings and delve into their specific applications within hospitals, including AI-assisted diagnosis, robotinitoring, and data analytics. A key finding is the insufficient use of KI to date in terms of promoting sustainable development in hospitals. Furthermore, attempts to analyze the potential benefits and challenges associated with these technologies in terms of enhancing patient outcomes, operational efficiency, cost savings, and differentiation from competitors. Drawing upon a comprehensive review of the existing literature and case studies, this paper provides valuable insights into the transformative potential of AI and robotics in hospitals.

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Copyright of Computer und Recht is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer und Recht is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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