Despite recent advancements, real-world use of Artificial Intelligence (AI) in radiology remains low, often due to the mismatch between AI offerings and the situated challenges faced by healthcare professionals. To bridge this gap, we conducted a field study at nine medical sites in Denmark and Kenya with two goals: (1) to understand the challenges faced by radiologists during chest X-ray practice and (2) to envision alternative AI futures that align with collaborative clinical work. This study uniquely grounds the AI design insights in the comprehensive characterisation of diagnostic work across multiple geographical and institutional contexts. Building on ideas articulated by interviewed radiologists (N = 18), we conceptualised five visions that transcend the traditional notions of AI support. These visions emphasise that the clinical usefulness of AI-based systems depends on their configurability and flexibility across three dimensions: type of clinical site, expertise of medical professionals, and situational and patient contexts. Addressing these dependencies requires expanding the clinical AI design space by envisioning futures rooted in the realities of practice rather than solely following the trajectory of AI development.

Artificial Intelligence (AI) repeatedly match or outperform radiologists in lab experiments. However, real-world implementations of radiological AI-based systems are found to provide little to no clinical value. This paper explores how to design AI for clinical usefulness in different contexts. We conducted 19 design sessions and design interventions with 13 radiologists from 7 clinical sites in Denmark and Kenya, based on three iterations of a functional AI-based prototype. Ten sociotechnical dependencies were identified as crucial for the design of AI in radiology. We conceptualised four technical dimensions that must be configured to the intended clinical context of use: AI functionality, AI medical focus, AI decision threshold, and AI Explainability. We present four design recommendations on how to address dependencies pertaining to the medical knowledge, clinic type, user expertise level, patient context, and user situation that condition the configuration of these technical dimensions.