Open Access

Background: The use of generative AI, as represented by ChatGPT, holds promising potential for nursing education. This manifests itself in various areas, including personalized learning, simulation training and teaching process support. However, its integration requires careful consideration of ethical implications, adaptation of curricula and a high level of digital competence on the part of teachers. Only in this way can potential risks, such as the distortion of knowledge, bias and educational inequalities, be avoided. Methods: Relevant publications were identified between 2019 and 2025 as part of a comprehensive literature search in the specialist databases PubMed, Embase, CINAHL and Scopus. The search was conducted using combined search terms that included the terms “generative AI”, “ChatGPT” and “nursing”. After removing duplicates and screening (PRISMA-guided), 140 full texts were analysed and divided into two publications. This rapid overview focuses on the topic of generative AI in nursing education. Results: As part of the analysis of the included studies, five thematic areas were identified, which were divided into the categories of nursing education, competence development and nursing skills, implementation possibilities, examination quality and ethical considerations, and evaluated. A key theme is the dual potential of this technology: it can enrich learning through features such as virtual tutors and improved exam preparation, but it also requires critical consideration of ethical issues such as plagiarism, data bias and the need for human oversight. Outlook: In this context, the conclusion emphasises the urgent need to adapt curricula and provide targeted further training for teachers so that GenAI can be used responsibly and effectively—rather than, as is often the case at present, by banning it altogether.

This article investigates, for the first time, how quickly curious individuals can uncover the secrets behind popular magic tricks through internet research requiring minimal effort. To do this, 20 well-known magic tricks were selected, and the underlying trick secrets were sought using the AI assistant ChatGPT. It was found that using language assistants and the large language models behind them makes the search significantly more promising compared to recent keyword-based searches. For 18 of the 20 magic tricks, an explanation in the form of a descriptive text or an exposure video was found with little effort. The significance of this observation for the art of magic was not the main focus of this contribution, but the concluding section at least contains the author's initial thoughts on the matter.

This work focuses on the detection and localization of small spherical fiducial markers in magnetic resonance imaging (MRI) using neural networks. Two image processing pipelines based on U-Net and YOLO architectures were developed and evaluated on a data set of T1- and T2-weighted MRI with voxel sizes ranging from 0.6 to 1.6 mm. Detection performance is evaluated using the F1-score, whereas localization is evaluated using two metrics that describe the deviation of the predicted position from the true position. Although the benchmark method, a conventional image processing pipeline based on connected component analysis achieved marginally lower positioning errors, the neural network approaches outperformed it in terms of detection performance, especially by reducing false negatives. The results show that both pipelines achieve accurate marker detection and localization, with U-Net slightly outperforming YOLO in terms of positioning accuracy. A key advantage of the neural network-based pipelines is their ability to handle markers with non-uniform or incomplete appearance, which enhances their robustness in real-world scenarios and provides flexibility by eliminating the need for manual parameter adjustments. While neural networks offer the advantage that they can be easily adapted to various imaging conditions, their dependence on training data can be a limitation. The results suggest that neural network based pipelines offer a robust alternative for fiducial marker detection and localization.

Based on Welzl's algorithm for smallest circles and spheres we develop a simple linear time algorithm for finding the smallest circle enclosing a point cloud on a sphere. The algorithm yields correct results as long as the point cloud is contained in a hemisphere, but the hemisphere does not have to be known in advance and the algorithm automatically detects whether the hemisphere assumption is met. For the full-sphere case, that is, if the point cloud is not contained in a hemisphere, we provide hints on how to adapt existing linearithmic time algorithms for spherical Voronoi diagrams to find the smallest enclosing circle.

When faced with a large number of reviews, customers can easily be overwhelmed by information overload. To address this problem, review systems have introduced design features aimed at improving the scanning, reading, and processing of online reviews. Though previous research has examined the effect of selected design features on information overload, a comprehensive and up-to-date overview of these features remains outstanding. We therefore develop and evaluate a taxonomy for information search and processing in online review systems. Based on a sample of 65 review systems, drawn from a variety of online platform environments, our taxonomy presents 50 distinct characteristics alongside the knowledge status quo of the features currently implemented. Our study enables both scholars and practitioners to better understand, compare and further analyze the (potential) effects that specific design features, and their combinations, have on information overload, and to use these features accordingly to improve online review systems for consumers.