# diagnostic radiology

The open-weight radiology decision model RadKev-27B raised task-averaged accuracy across fifteen radiology decision tasks from 75.9% to 80.8%, and report-error detection from 72.1% to 94.7%, while answering at roughly 1/132 the latency of a reasoning LLM.

The open-weight radiology decision model RadKev-27B raised task-averaged accuracy across fifteen radiology decision tasks from 75.9% to 80.8%, and report-error detection from 72.1% to 94.7%, while answering at roughly 1/132 the latency of a reasoning LLM.

This article argues 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, thereby preserving the pre-AI interpretation as an auditable event, while noting that responsibility remains with the radiologist who signs the report and that the proposal is bounded by what each device is cleared to do.

This article argues 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, thereby preserving the pre-AI interpretation as an auditable event, while noting that responsibility remains with the radiologist who signs the report and that the proposal is bounded by what each device is cleared to do.