AI has long been regarded as a panacea for decision-making and many other aspects of knowledge work; as something that will help humans get rid of their shortcomings. We believe that AI can be a useful asset to support decision-makers, but not that it should replace decision-makers. Decision-making uses algorithmic analysis, but it is not solely algorithmic analysis; it also involves other factors, many of which are very human, such as creativity, intuition, emotions, feelings, and value judgments. We have conducted semi-structured open-ended research interviews with 17 dermatologists to understand what they expect from an AI application to deliver to medical diagnosis. We have found four aggregate dimensions along which the thinking of dermatologists can be described: the ways in which our participants chose to interact with AI, responsibility, ‘explainability’, and the new way of thinking (mindset) needed for working with AI. We believe that our findings will help physicians who might consider using AI in their diagnosis to understand how to use AI beneficially. It will also be useful for AI vendors in improving their understanding of how medics want to use AI in diagnosis. Further research will be needed to examine if our findings have relevance in the wider medical field and beyond.

Human-AI collaboration is an increasingly important area of research as AI systems are integrated into everyday workflows and moving beyond mere automation and augmentation to more collaborative roles. However, existing research often overlooks the dynamics and performance aspects of this interaction. Our study addresses this gap through a review of empirical AI studies from 2018–2024, focusing on the key factors influencing human-AI collaboration outcomes within the spectrum of Human-Centered Artificial Intelligence (HCAI). We identify 24 critical performance factors that influence hybrid performance, grouped into four categories using thematic analysis. Then, we uncover and analyze the complex, non-linear interdependencies between these factors. We present these relationships in a factor dependency graph, highlighting the most influential nodes. The graph and specific factor interactions supported by the papers reveal a quite complex web, an interconnectedness of factors. As opposed to being an easy-to-predict combination of inputs, human-AI collaboration in a given context likely leads to a dynamic, evolving system with often non-linear effects on its hybrid performance. Our findings and the previous research on automation technologies suggest that the application of AI tools in collaborative scenarios would benefit from a comprehensive performance framework. Our study intends to contribute to this future line of research with this initial framework.