Computer Vision News Computer Vision News 48 Congrats, Doctor Hubert! Despite recent advancements, e.g., in pathology detection in X-rays, MRIs and CTs, AI-based systems remain notably absent in current clinical practice. One key factor contributing to this gap is the prevalent technology-centric approach to AI innovation, which often results in the limited clinical usefulness of AI-based support systems. To address this issue I conducted research within the framework of an interdisciplinary project aimed at creating a chest Xray support tool for radiologists in Denmark and Kenya. Based on a systematic literature review (Zając et al. 2023, TOCHI), I find that challenges afflicting the realization of clinical AI in practice stem not from a single issue but rather from sociotechnical interdependencies present when introducing AI into a clinical context. For example, how a clinical position affects the need for explanations. I argue that addressing these challenges necessitates close collaboration among stakeholders with expertise in HCI, AI, and healthcare throughout the innovation processes. Hubert D. Zając holds a PhD in Computer Science from the University of Copenhagen. His PhD focused on making clinical AI useful in practice. Now, Hubert is working as a Postdoc in Human-Centered AI at the University of Copenhagen developing a holistic approach to AI innovation encompassing the entirety of the development process: from data creation to onboarding. Figure 1: Evolution of the developed chest X-ray support system.
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