Martin Jansen

Tirtha Chanda, Sarah Haggenmueller, Tabea-Clara Bucher et al.

Artificial intelligence (AI) systems have substantially improved dermatologists' diagnostic accuracy for melanoma, with explainable AI (XAI) systems further enhancing clinicians' confidence and trust in AI-driven decisions. Despite these advancements, there remains a critical need for objective evaluation of how dermatologists engage with both AI and XAI tools. In this study, 76 dermatologists participated in a reader study, diagnosing 16 dermoscopic images of melanomas and nevi using an XAI system that provides detailed, domain-specific explanations. Eye-tracking technology was employed to assess their interactions. Diagnostic performance was compared with that of a standard AI system lacking explanatory features. Our findings reveal that XAI systems improved balanced diagnostic accuracy by 2.8 percentage points relative to standard AI. Moreover, diagnostic disagreements with AI/XAI systems and complex lesions were associated with elevated cognitive load, as evidenced by increased ocular fixations. These insights have significant implications for clinical practice, the design of AI tools for visual tasks, and the broader development of XAI in medical diagnostics.

Tim Gülke, Bernhard Rumpe, Martin Jansen et al.

Effective requirements management plays an important role when it comes to the support of product development teams in the automotive industry. A precise positioning of new cars in the market is based on features and characteristics described as requirements as well as on costs and profits. [Question/problem] However, introducing or changing requirements does not only impact the product and its parts, but may lead to overhead costs in the OEM due to increased complexity. The raised overhead costs may well exceed expected gains or costs from the changed requirements. [Principal ideas/results] By connecting requirements with direct and overhead costs, decision making based on requirements could become more valuable. [Contribution] This problem statement results from a detailed examination of the effects of requirements management practices on process complexity and vice versa as well as on how today's requirements management tools assist in this respect. We present findings from a joined research project of RWTH Aachen University and Volkswagen