Understanding AI Algorithms in Medical Imaging: A Radiologist's Guide to Models and Methods
TL;DR
This review provides a clinician-oriented framework for understanding imaging AI by covering core model architectures, task-based applications, workflow integration, and the practical interpretation of algorithmic outputs.
Abstract
Abstract Artificial intelligence is now embedded in radiology workflows across detection, triage, quantification, and reporting. Yet, most clinicians deploy these tools without a working understanding of how their outputs are generated or where they reliably fail. Unlike conventional rule-based clinical workflows, modern imaging AI systems generate probabilistic outputs whose reliability depends on training data, task definition, and deployment context. Radiologists must understand what these systems actually produce, know their predictable failure points, and never allow a confidence score to replace the clinical reasoning that only a trained human can apply. This review provides a clinician-oriented framework for understanding imaging AI by covering core model architectures, task-based applications, workflow integration, and the practical interpretation of algorithmic outputs. This is important because as imaging AI scales across institutions and populations, the radiologist's capacity to interrogate, contextualize, and, where necessary, override algorithmic outputs becomes not just a clinical skill but a professional responsibility.