ReXPref-Prior: A MIMIC-CXR Preference Dataset for Reducing Hallucinated Prior Exams in Radiology Report Generation

Generative vision-language models have exciting potential implications for
radiology report generation, but unfortunately such models are also known to
produce hallucinations and other nonsensical statements. For example,
radiology report generation models regularly hallucinate prior exams, making
statements such as "The lungs are hyperinflated with emphysematous changes as
seen on prior CT" despite not having access to any prior exam. To address this
shortcoming, we propose ReXPref-Prior, an adapted version of MIMIC-CXR where
GPT-4 has removed references to prior exams from both findings and impression
sections of chest X-ray reports. We expect ReXPref-Prior will be useful for
training models that hallucinate prior exams less frequently, through
techniques such as direct preference optimization. Additionally, ReXPref-
Prior's validation and test sets can be used as a new benchmark for evaluating
report generation models.