It is argued that radiology should treat sequence as a design variable in routine clinical practice: capture what the radiologist concluded before AI exposure, then what the AI displayed and how the radiologist responded.
Abstract
Artificial intelligence is being embedded in diagnostic radiology faster than the workflows around it have been designed. When an AI result is displayed together with the image, the radiologist's independent interpretation and their AI-influenced interpretation collapse into a single, blended record, and automation-bias research shows that collapse is consequential, not merely theoretical. The problem is not only whether AI changes diagnostic accuracy, but whether the workflow preserves a durable record of independent clinical judgment before AI exposure. I argue that radiology should treat sequence as a design variable in routine clinical practice: capture what the radiologist concluded before AI exposure, then what the AI displayed and how the radiologist responded. The proposal does not redistribute responsibility, which remains with the radiologist who signs the report, and it is bounded by what each device is cleared to do. The underlying principle is not new: statistical instruments have known error characteristics, and human judgment should be evaluated in relation to them. What is new is the ability to preserve the radiologist's pre-AI interpretation as an auditable event.
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.
Sharad Maheshwari, Sachin Kumar· Indian Journal of Radiology...· 0 citations
Artificial intelligence (AI) has become one of the most actively discussed tools in modern day radiology, promising to help interpret X-rays, CT scans, and MRIs alongside human radiologists. It has matched or exceeded human accuracy outright, particularly in a few narrow tasks. Furthermore, AI has come at a time when i...
This Perspective defines radiology as an epistemic system: the organized clinical infrastructure through which imaging observations become warranted, actionable, revisable, and accountable knowledge.
A narrative synthesis of the human-AI interaction and radiology AI literature highlighted three underrecognized determinants of successful human-AI collaboration in radiology, and concrete research directions are proposed to bridge the gap between algorithmic capabilities and clinical utility.
Su Hwan Kim, L. Adams, B. Wiestler et al.· Radiology: Artificial Intell...· 0 citations
Agentic AI, including multi-agent architectures, could reshape how radiology departments operate, but the field needs prospective, multi-center trials with standardized endpoints before claiming it already has.
Wei Yi, Ya-Juan Chen, Xiao Feng et al.· Frontiers in Medicine· 0 citations
Abstract Artificial intelligence (AI) is entering Indian radiology faster than the capacity of existing workflows to adapt, with increasing claims of expert-level algorithmic performance, a regulatory architecture beginning to form, and rising clinician interest. Yet translation of technically capable tools into reliab...
Suvrankar Datta, Prerna Priyadarshini, A. Rekhi· Indian Journal of Radiology...· 0 citations
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