  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.
