Who Read It First? Documenting Independent Judgment in AI-Assisted Radiology

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.