From Isolated Intelligence to Interpretive Infrastructure in Radiology AI

Radiology has more regulatory-cleared artificial intelligence tools than any other medical specialty, yet they have delivered less consistent clinical impact than their benchmark performance suggested. We argue that the dominant response, scaling perceptual models and extending them toward autonomous agency, mistakes the nature of the problem. The limitation is architectural, not algorithmic: current systems perform isolated perception while remaining disconnected from the temporal, contextual, and experiential integration that interpretation requires. We trace this to a fundamental simulation gap, the absence of the counterfactual signal that lets AI reach superhuman performance in other domains, and argue that scaling alone cannot close it. In response, we outline a hybrid orchestration layer, grounded in established clinical frameworks, that assembles a comprehensive evidence base for the radiologist while keeping generative components constrained and interpretive judgment human. The shift is one of design paradigm: from isolated tools deployed alongside the radiologist to interpretive infrastructure that composes validated outputs into coherent evidence without anchoring the reader's own assessment.