Artificial intelligence is increasingly deployed in healthcare. Radiology is one of the most active areas of clinical AI development. Despite substantial investments, many AI systems struggle to gain traction in clinical practice. The issues are rarely purely technical; they concern how AI tools fit into existing workflows, how they interact with professional responsibilities, and how they sit within institutional arrangements. It also remains unclear how core values in AI research such as explainability, value alignment, accountability, and trust are negotiated in clinical work. Established Health Technology Assessment (HTA) models define assessment domains but offer limited guidance on implementation: how technologies are staged within workflows, how responsibilities are redistributed, and how local conditions redefine value for different stakeholders. This paper presents the AI in PRACTICE framework, a five-phase methodology grounded in Techno-Anthropology and its relational ontology, for examining the situated work through which AI becomes part of clinical practice. Explainability, value alignment, trust, and distributed responsibility are understood as produced through socio-technical practices. The framework complements existing HTA models through iterative engagement and stakeholder participation. Insights are illustrated through three radiology-facing AI projects showing how assessment criteria are enacted, resisted, and revised in context, and how the framework generates practice-based artefacts.