Open Access

Heart failure with preserved ejection fraction (HFpEF) remains challenging to diagnose due to the complexity of diastolic function assessment during stress echocardiography, where multiple hemodynamic parameters must be evaluated under time pressure. Explainable artificial intelligence, specifically rule-based Clinical Decision Support Systems (CDSS), offers promising improvements in reproducibility and interpretability. Methods: A rule-based CDSS was developed and clinically validated to automate left ventricular diastolic function assessment during semi-supine bicycle stress echocardiography. A prospective cohort of 134 patients (mean age 61.3 ± 8.7 years) with exertional dyspnea and preserved left ventricular ejection fraction (LVEF &amp;gt;50%) was enrolled, excluding individuals with significant valvular pathologies, arrhythmias, or unstable ischemia. Echocardiographic and Doppler data were collected using Toshiba Aplio500 and Esaote MyLabSIGMA systems. The algorithm incorporated manual input of measurements, computed derived indices (e.g., diastolic reserve index, myocardial stiffness, vascular resistance), and applied rule-based logic in accordance with ASE/EACVI (2016/2022) guidelines and the ESC HFpEF consensus. Results: The CDSS generated diagnostic conclusions within 3 min per case, matching expert assessments in 93% of cases and correctly identifying stress-induced diastolic dysfunction in 85%. It demonstrated high diagnostic agreement (ICC &amp;gt; 0.94) and discrimination (AUC = 0.92). Rule-based outputs, such as “Impaired diastolic reserve” or “Right ventricular dysfunction under load,” were based on combinations of parameters (e.g., E/e′ > 15, Δe′ ≤ 0, TAPSE < 17 mm, PCWR > 12 mmHg). Conclusion: The explainable, guideline-compliant CDSS enables real-time, transparent analysis of diastolic function, supporting improved diagnostic consistency and augmented physician decision-making in cardiovascular care.

The rapid development of Artificial Intelligence (AI) has profoundly transformed translation practices and poses new challenges for higher education. This article presents a multi-stage teaching project designed for MA students that critically explores the potentials and limitations of AI-based translation tools. Through the comparative analysis of literary and contemporary texts—most notably Franz Kafka’s short prose piece “Gib’s auf ”—students examine outputs from tools such as DeepL, Google Translate, ChatGPT, and Matecat. The project combines text analysis, comparison of machine translations, post-editing, and collaborative translation workshops, including direct interaction with a contemporary author. Results show that while AI tools provide efficient and often accurate support, they remain limited in conveying stylistic nuance, pragmatics, and cultural meaning. The study demonstrates that translation quality depends on human interpretation, creativity, and responsibility, highlighting AI as a didactic catalyst rather than a substitute for professional translational competence.

In this article, I present changes to the content of the Bachelor’s degree programme German Business Communication that I consider necessary in the light of AI-driven developments both in science and economic fields. I focus on developing critical thinking when students use Generative Artificial Intelligence (GenAI) in their scientific work. This fits in well with the programme’s critical and engaged academic approach. The article sets out with an empirical consideration of AI in academic settings and GenAI application within the framework of the degree programme. Based upon this, a teaching/learning unit on GenAI use in a module of the programme is presented, which was designed and implemented in the winter semester 2025/26. The preliminary results reveal that further considerations are required to expand teaching units on AI tools for specific areas of business communication.

The genre “presentation” represents a central communicative act in academic settings, particularly in foreign-language and subject-specific language courses accompanying academic studies, where technical content must be appropriately adapted to the target audience in oral format. However, it is precisely in this context that a special focus is needed on the influence and potential of AI in the creation and implementation of presentations. The objective of this article is to discuss how AI-tools — such as those for text generation, visualization, or language analysis — can support the creation, delivery, and reflection of presentations, but also what negative effects they may have on the development of students’ presentation skills in specialized and foreign language university courses. An overview of relevant AI tools that can also be applied in later professional presentation practice and is intended to provide teachers and learners with a practical repertoire of digital aids. The findings provide impetus for the responsible and skill-oriented integration of AI into foreign language teaching.

In today’s rapidly evolving commercial landscape, manufacturing enterprises face significant challenges in maintaining resilience amid disruptions such as pandemics, natural disasters, and geopolitical conflicts. To address these challenges, we introduce a novel GPT-based early detection tool designed for real-time supply chain risk assessment. This system integrates proprietary company data, including supply chain portfolios, with publicly available information, such as news articles, to estimate risk scores for respective supply chains, thereby enhancing decision-making processes. Leveraging advanced machine learning techniques–Generative Pretrained Transformers (GPT), zero-shot learning, and structured outputs–the tool operates locally to ensure data privacy and minimize information leakage. Utilizing the "news-please" crawler and the "Llama 3.1" GPT model, the system continuously monitors selected media sources, providing timely risk assessments. Our research demonstrates the tool’s potential to enhance proactive risk management in supply chains, validated through testing on both real and augmented datasets. By evaluating four exemplary supply chains, we characterize the tool’s capability to support decision-making in unpredictable global environments. The results indicate that, while the system occasionally exhibits oversensitivity, it consistently aids in identifying critical events that may impact supply chain operations. Future developments will focus on refining the tool’s accuracy and expanding its applications, particularly in monitoring regulatory changes.

In this paper, the authors present a freely accessible resource covering German light verb constructions from the domain of administrative and business language (such as “Bericht erstatten” (to report)) together with their associated verb frames (in particular objects and governing prepositions). These constructions, which in many cases represent overly complex and difficult-to-read language, are joined within the same resource by simple verbs, also together with their respective verb frames. This not only provides a type of thesaurus for light verb constructions that can assist in text simplification, but the documented verb frames can also support the automatic generation of grammatically correct suggestions for simpler textual alternatives. (Abstract ist formal geringfügig verändert gegenüber dem Original.)

Rezension zu: Buck, Isabella: Wissenschaftliches Schreiben mit KI. Tübingen: Narr Francke Attempto Verlag, 2025, 242 Seiten. ISBN: 978-3-8385-6365-7.