Paper ID: 2412.02971 • Published Dec 4, 2024
MedAutoCorrect: Image-Conditioned Autocorrection in Medical Reporting
Arnold Caleb Asiimwe, Dídac Surís, Pranav Rajpurkar, Carl Vondrick
TL;DR
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In medical reporting, the accuracy of radiological reports, whether generated
by humans or machine learning algorithms, is critical. We tackle a new task in
this paper: image-conditioned autocorrection of inaccuracies within these
reports. Using the MIMIC-CXR dataset, we first intentionally introduce a
diverse range of errors into reports. Subsequently, we propose a two-stage
framework capable of pinpointing these errors and then making corrections,
simulating an \textit{autocorrection} process. This method aims to address the
shortcomings of existing automated medical reporting systems, like factual
errors and incorrect conclusions, enhancing report reliability in vital
healthcare applications. Importantly, our approach could serve as a guardrail,
ensuring the accuracy and trustworthiness of automated report generation.
Experiments on established datasets and state of the art report generation
models validate this method's potential in correcting medical reporting errors.