Automation Bias in AI-Decision Support: Results from an Empirical Study.

Introduction: Automation bias poses a significant challenge to the effectiveness of Clinical Decision Support Systems (CDSS), potentially compromising diagnostic accuracy. Previous research highlights trust, self-confidence, and task difficulty as key determinants. With the increasing availability of AI-enabled CDSS, automation bias attains new attention.

Lessons Learned from an Interprofessional European Summer School in Health Informatics.

The aim of this European interprofessional Health Informatics (HI) Summer School was (i) to make advanced healthcare students familiar with what HI can offer in terms of knowledge development for patient care and (ii) to give them an idea about the underlying technical and legal mechanisms. According to the students' evaluation, interprofessional education was very well received, problem-based learning focussing on cases was rated positively and the learning goals were met. However, it was criticised that the online material provided was rather detailed and comprehensive and could have been a bit overcharging for beginners.

Assessing readiness for childhood obesity prevention in German municipalities: application of the community readiness model.

The World Health Organization has identified childhood obesity as one of the most serious public health problems of the 21st century. Understanding a municipality's readiness to address it is crucial to achieve successful interventions. However, the preparedness of German municipalities to address childhood obesity has not yet been investigated.

Trust in Digitalization and AI: Findings from a Qualitative Study on Healthcare Professionals in Germany.

The acceptance and use of digital technologies depend on the trustworthiness attributed to them. Experts were interviewed about how they assign trust to digital technologies or AI (N=12). The data were analyzed applying the focused qualitative content analysis.

The Representation of Trust in Artificial Intelligence Healthcare Research.

Artificial intelligence (AI) tends to emerge as a relevant component of medical care, previously reserved for medical experts. A key factor for the utilization of AI is the user's trust in the AI itself, respectively the AIt's decision process, but AI-models are lacking information about this process, the so-called Black Box, potentially affecting usert's trust in AI. This analysis' objective is the description of trust-related research regarding AI-models and the relevance of trust in comparison to other AI-related research topics in healthcare.