The Impact of Auxiliary Patient Data on Automated Chest X-Ray Report Generation and How to Incorporate It (2025.acl-long)
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| Challenge: | Traditionally, CXR report generation relies on data from a patient’s exam, overlooking valuable information from patient electronic health records. |
| Approach: | They propose to integrate patient data from ED records into multimodal language models that embed patient data into a language model. |
| Outcome: | The proposed model incorporates patient data from the MIMIC-CXR and MIMICIV-ED datasets to improve diagnostic accuracy and improves radiologist effectiveness. |
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| Challenge: | Existing medical report generation efforts focus on producing human-readable reports, yet the generated text may not be well aligned to the clinical facts. |
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| Challenge: | Radiology reports are detailed text descriptions of the content of medical scans. |
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| Challenge: | Existing studies do not consider the complex structure information between and within report sections. |
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| Challenge: | Recent studies show that learning-based models fail to accurately capture and describe abnormal regions due to data bias. |
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| Challenge: | Existing approaches to improve radiology reports are limited due to the high cost of manual simplification. |
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| Challenge: | Medical report generation is an important medical artificial intelligence task. |
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| Challenge: | Existing methods for radiology report generation fail to incorporate prior knowledge . data bias, sparse features of chest X-ray image make it difficult to generate reports . |
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| Challenge: | Clinical practice frequently uses medical imaging for diagnosis and treatment. |
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Jean-Benoit Delbrouck, Justin Xu, Johannes Moll, Alois Thomas, Zhihong Chen, Sophie Ostmeier, Asfandyar Azhar, Kelvin Zhenghao Li, Andrew Johnston, Christian Bluethgen, Eduardo Pontes Reis, Mohamed S Muneer, Maya Varma, Curtis Langlotz
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