From signal to reasoning: Computational bottlenecks in the evolution of artificial intelligence in radiology.

Abstract

Artificial intelligence (AI) in radiology is often described as a sequence of architectures. This conceptual narrative review instead organizes its evolution around two questions: Which computational constraint was relaxed, and where did the resulting capability enter the radiologic chain from signal formation to recommendation? We identify five analytical epochs: handcrafted computer-aided detection, deep and volumetric learning, a branching stage of global-context modeling and learned reconstruction, multimodal foundation models, and an emerging stage of inference-stage reasoning. Epochs I-IV are characterized primarily by changes in representation or image formation; Epoch V shifts the frontier toward adaptive case-specific inference, for which radiology-specific evidence remains largely preclinical. The framework also generates a clinical prediction: Verification should be matched to the level of participation and its characteristic failure mode. Existing studies provide partial empirical support, but later-stage verification requirements remain framework-derived proposals rather than established standards.