Effective communication is vital in healthcare, especially in complex areas like prostate cancer diagnosis. Although there has been much research on communication among different stakeholders in healthcare, there is still a visible gap in addressing the different information exchange needs of doctors and patients, and how these can impact their communication during prostate cancer diagnosis. This paper explores these challenges from both patients' and doctors' - more specifically, radiologists' - perspectives. Through contextual inquiries and interviews with experienced radiologists, who play a key role in diagnosis, and patients with prostate cancer, we have gained valuable insights into current communication practices associated with the context and explored the potential of technology to support them. Our findings highlight the challenges faced by both stakeholders during diagnosis and their needs and preferences for improved communication. Based on those findings, we propose design implications for computer technologies to enhance communication among stakeholders, ultimately improving collaboration, efficiency, and patient outcomes in prostate cancer care.

This paper examines the potential of Human-Centered AI (HCAI) solutions to support radiologists in diagnosing prostate cancer. Prostate cancer is one of the most prevalent and increasing cancers among men. The scarcity of radiologists raises concerns about their ability to address the growing demand for prostate cancer diagnosis, leading to a significant surge in the workload of radiologists. Drawing on an HCAI approach, we sought to understand the current practices concerning radiologists’ work on detecting and diagnosing prostate cancer, as well as the challenges they face. The findings from our empirical studies point toward the potential that AI has to expedite informed decision-making and enhance accuracy, efficiency, and consistency. This is particularly beneficial for collaborative prostate cancer diagnosis processes. We discuss these results and introduce design recommendations and HCAI concepts for the domain of prostate cancer diagnosis, with the aim of amplifying the professional capabilities of radiologists.

Most participation processes in urban planning offer poor incentives, especially for young citizens, hence important citizen's needs are excluded. Our work aims at identifying the degree to which Augmented Reality (AR) might motivate young people. We developed an AR-app with Unity3D to create new interaction concepts for use cases in urban planning. Building projects and environment changes are visualized, so citizens can contribute design ideas to the process. Using a human-centered design approach, we invited different stakeholders to participate. We conducted 40 interviews and a survey, then interaction concepts were evolved by citizens in four participatory design workshops. Our findings show that AR can motivate increased participation in urban planning. We also demonstrate a new approach to engaging low-tech users in designing high-tech solutions such as AR systems by using haptic 3D-tools like Lego or clay. Furthermore, we propose ways in which AR could be used collaboratively and embedded in existing participation processes.

Over the last decades, different kinds of design guides have been created to maintain consistency and usability in interactive system development. However, in the case of spatial applications, practitioners from research and industry either have difficulty finding them or perceive such guides as lacking relevance, practicability, and applicability. This paper presents the current state of scientific research and industry practice by investigating currently used design recommendations for mixed reality (MR) system development. We analyzed and compared 875 design recommendations for MR applications elicited from 89 scientific papers and documentation from six industry practitioners in a literature review. In doing so, we identified differences regarding four key topics: Focus on unique MR design challenges, abstraction regarding devices and ecosystems, level of detail and abstraction of content, and covered topics. Based on that, we contribute to the MR design research by providing three factors for perceived irrelevance and six main implications for design recommendations that are applicable in scientific and industry practice.