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

Authors

Şükrü Mehmet Ertürk, Sıtkı Safa Taflan

Published in

Diagnostic and interventional radiology (Ankara, Turkey). Sep 30, 2026. Epub Sep 30, 2026.

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.

PMID:
42813600
Bibliographic data and abstract were imported from PubMed on 30 Sep 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.