<p>The implementation of artificial intelligence (AI) in radiology has garnered significant attention across both research and practice over the last decade. However, opinions on whether AI will augment or replace radiologists in their work have so far been divided, and the debate around this continues to linger. This article conceptually structures and terms this phenomenon as the “replacement-augmentation paradox” of AI in radiology—the persistent coexistence of replacement fears and augmentation perceptions within the same professional community. Rather than treating these positions as mutually exclusive or empirically resolvable through performance metrics alone, this article conceptualises the replacement-augmentation paradox as a synthesising framework that structures and interprets divergent, evidence-informed narratives of AI integration in radiology. Drawing predominantly on sociotechnical and perception-based research, this article juxtaposes the two coexistent narratives and synthesises collective observations to shed light on relatively less emphasised aspects shaping replacement-augmentation debates. The findings obtained here explicate the implementation of AI tools (such as deep learning) for medical imaging and early diagnosis in radiology through a proposed “techno-perceptual” framework where the augmentation and replacement of the existing work practices are both plausible outcomes (O), subject to preconditions. Alleviation of fears and determination of the resultant narrative is thus linked with the provision of three jointly enabling conditions: training (T), evidence (E), and decisional control (C), the presence or absence of which are deemed to be directly consequential in terms of yielding perceptions of augmentation or replacement, respectively. The importance and interplay of these factors are delineated here through the novel Boolean equation O = T ∧ E ∧ C, a corresponding logical table, a process flow diagram, and a radiologist typology matrix depicting four professional archetypes.</p>