Correctly Parsed, Wrong Lesion: Where Target Mix-Ups Arise When AI Binds Measurement Requests to Lung CT Nodules—A Staged, Hypothesis-Generating Evaluation

AI systems that measure lesions on request can compute correctly on the wrong lesion, an outcome no benchmark I found scores. I located where such mix-ups arise, in three stages on public lung CTs. A stratified audit of 1785 DICOM segmentation series found identical coded semantics for all 5192 pairs of different nodules on one scan; nodule numbers existed but needed linking to the request. A gate trusting structural checks authorized all 200 constructed requests, 89 for a mismatched and 41 for an ambiguous target. An expert wrote 60 requests in three sets, with each design frozen first. A rule parser carried a misread rank (“longest diameter” read as “largest”) in every resolution; removing it post hoc kept 13 of 26 correct resolutions, and agreement with a choice-constrained model parser removed all 25 rank-dependent wrong ones. A whole-lung definition of “lateral” excluded an intended nodule. In the confirmatory set, both pre-specified consensus gates authorized the nearest unintended nodule in two of 20 requests. Adjudication found three requests naming a lobe the images contradicted and two unassignable lobes; no map error was demonstrated. Parser agreement did not catch a request–image discordance. Clinical safety and workflow benefit were not evaluated.