Adrian Krenzer

This paper explores the potential of a German-language chatbot to engage users in argumentative dialogues on ethically sensitive topics. Utilizing an argumentative knowledge graph, the chatbot is equipped to engage in discussions on the ethical implications of autonomous AI systems in hypothetical future scenarios in the fields of medicine, law, and self-driving cars. In a study with 178 student participants, we investigated the chatbot’s argumentation effect—its ability to offer new perspectives, gain user acceptance, and broaden users’ viewpoints on complex issues. The results indicated a substantial argumentation effect, with 13–21% of participants shifting their opinions to more moderate stances after interacting with the chatbot. This shift demonstrates the system’s effectiveness in fostering informed discourse and increasing users’ understanding of AI ethics. While the chatbot was well-received, with users acknowledging the quality of its arguments, we identified opportunities for improvement in its argument recognition capabilities. Despite this, our results indicate the chatbot’s potential as an educational tool in engaging users with the ethical dimensions of AI technology and promoting informed discourse.

This study presents a German-language AI chatbot adept at fostering argumentative dialogues across various topics. A core component is the Argumentative Knowledge Graph that powers argument understanding and generation. We showcase the Argumentation Effect of our chatbot in a comprehensive user study. This effect is characterized by the chatbot's successful ability to reshape users' attitudes towards ethically challenging topics, such as autonomous AI systems acting as medicine doctors, by imparting novel perspectives and insights. This led to a substantial gain in user knowledge and acceptance, underscoring the transformative role dialogue systems can play in promoting more informed discussions on complex ethical matters. In tandem with this, we introduced a preliminary methodology for advancing the chatbot's Argument Recognition Unit (ARU). By harnessing real-world user arguments and generated samples from an advanced language model, we achieved significant improvements in the confidence and accuracy of argument prediction. In summary, our research demonstrates how an AI chatbot can instigate a powerful Argumentation Effect, reshaping attitudes, widening knowledge, and facilitating informed discourse on ethically charged topics. This is further amplified by improved argument recognition capabilities, opening promising avenues for more effective, and thus more engaging, user-chatbot dialogues.

Background and study aims Artificial intelligence (AI)-based systems for computer-aided detection (CADe) of polyps receive regular updates and occasionally offer customizable detection thresholds, both of which impact their performance, but little is known about these effects. This study aimed to compare the performance of different CADe systems on the same benchmark dataset. Methods 101 colonoscopy videos were used as benchmark. Each video frame with a visible polyp was manually annotated with bounding boxes, resulting in 129 705 polyp images. The videos were then analyzed by three different CADe systems, representing five conditions: two versions of GI Genius, Endo-AID with detection Types A and B, and EndoMind, a freely available system. Evaluation included an analysis of sensitivity and false-positive rate, among other metrics. Results Endo-AID detection Type A, the earlier version of GI Genius, and EndoMind detected all 93 polyps. Both the later version of GI Genius and Endo-AID Type B missed 1 polyp. The mean per-frame sensitivities were 50.63 % and 67.85 %, respectively, for the earlier and later versions of GI Genius, 65.60 % and 52.95 %, respectively, for Endo-AID Types A and B, and 60.22 % for EndoMind. Conclusions This study compares the performance of different CADe systems, different updates, and different configuration modes. This might help clinicians to select the most appropriate system for their specific needs.

Background: Colorectal cancer is a leading cause of cancer-related deaths worldwide. The best method to prevent CRC is a colonoscopy. However, not all colon polyps have the risk of becoming cancerous. Therefore, polyps are classified using different classification systems. After the classification, further treatment and procedures are based on the classification of the polyp. Nevertheless, classification is not easy. Therefore, we suggest two novel automated classifications system assisting gastroenterologists in classifying polyps based on the NICE and Paris classification. Methods: We build two classification systems. One is classifying polyps based on their shape (Paris). The other classifies polyps based on their texture and surface patterns (NICE). A two-step process for the Paris classification is introduced: First, detecting and cropping the polyp on the image, and secondly, classifying the polyp based on the cropped area with a transformer network. For the NICE classification, we design a few-shot learning algorithm based on the Deep Metric Learning approach. The algorithm creates an embedding space for polyps, which allows classification from a few examples to account for the data scarcity of NICE annotated images in our database. Results: For the Paris classification, we achieve an accuracy of 89.35 %, surpassing all papers in the literature and establishing a new state-of-the-art and baseline accuracy for other publications on a public data set. For the NICE classification, we achieve a competitive accuracy of 81.13 % and demonstrate thereby the viability of the few-shot learning paradigm in polyp classification in data-scarce environments. Additionally, we show different ablations of the algorithms. Finally, we further elaborate on the explainability of the system by showing heat maps of the neural network explaining neural activations. Conclusion: Overall we introduce two polyp classification systems to assist gastroenterologists. We achieve state-of-the-art performance in the Paris classification and demonstrate the viability of the few-shot learning paradigm in the NICE classification, addressing the prevalent data scarcity issues faced in medical machine learning.