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

The article states that 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 that automation-bias research shows this collapse "is consequential, not merely theoretical." It widens the question from whether AI changes diagnostic accuracy to whether the workflow preserves a durable record of independent clinical judgment before AI exposure, making the record structure itself the object of attention. This is an argumentative article that reasons from existing awareness in automation-bias research; it reports no new experimental data, sample sizes, or effect sizes.

The author 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. It establishes the pre-AI interpretation as something that can be preserved as an auditable event, which the text explicitly calls "new," whereas the underlying principle it rests on, that statistical instruments have known error characteristics and human judgment should be evaluated in relation to them, is described as not new. This is a normative design proposal advanced through argument; the text reports no implementation study or comparative data.

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. Alongside proposing a record-sequence design, it explicitly delimits responsibility and regulatory boundaries, keeping the proposal within existing accountability structures and device clearances. This is a stated qualification in the text, a position and boundary statement rather than an empirical result.

The proposal is meant for routine radiology clinical practice, in settings where AI results are displayed together with the image, and is bounded by what each device is cleared to do; it does not change responsibility, which remains with the radiologist who signs the report. For readers designing AI-assisted reading workflows, record structures, and audit mechanisms that preserve traces of independent judgment, this framework offers an actionable line of thinking.

A careful reader might still watch how recording the pre-AI interpretation as an auditable event would work in practice across specific devices and institutions; how sequence design affects reading behavior and documentation burden; and how far automation-bias findings apply across different radiology settings. The loaded text is at the summary level and does not include figures or data tables, so any such details in the original cannot be assessed here.