# Semantic Textual Similarity Assessment in Chest X-ray Reports Using a Domain-Specific Cosine-Based Metric

arXiv: https://arxiv.org/abs/2402.11908 · HTML: https://mlsanity.com/paper/2402.11908/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 7 of 7)

- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Generating Radiology Reports via Memory-driven Transformer](https://mlsanity.com/paper/2010.16056/) (2020)
- [CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT](https://mlsanity.com/paper/2004.09167/) (2020)
- [RadGraph: Extracting Clinical Entities and Relations from Radiology Reports](https://mlsanity.com/paper/2106.14463/) (2021)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation](https://mlsanity.com/paper/2010.10042/) (2020)

## Built on by (most cited first, 4 of 4)

- [Advances and Challenges in Semantic Textual Similarity: A Comprehensive Survey](https://mlsanity.com/paper/2601.03270/) (2025)
- [A Comparative Study of Recent Large Language Models on Generating Hospital Discharge Summaries for Lung Cancer Patients](https://mlsanity.com/paper/2411.03805/) (2024)
- [VICCA: Visual Interpretation and Comprehension of Chest X-ray Anomalies in Generated Report Without Human Feedback](https://mlsanity.com/paper/2501.17726/) (2025)
- [Semantic Similarity in Radiology Reports via LLMs and NER](https://mlsanity.com/paper/2510.03102/) (2025)

## Abstract (arXiv)

Medical language processing and deep learning techniques have emerged as critical tools for improving healthcare, particularly in the analysis of medical imaging and medical text data. These multimodal data fusion techniques help to improve the interpretation of medical imaging and lead to increased diagnostic accuracy, informed clinical decisions, and improved patient outcomes. The success of these models relies on the ability to extract and consolidate semantic information from clinical text. This paper addresses the need for more robust methods to evaluate the semantic content of medical reports. Conventional natural language processing approaches and metrics are initially designed for considering the semantic context in the natural language domain and machine translation, often failing to capture the complex semantic meanings inherent in medical content. In this study, we introduce a novel approach designed specifically for assessing the semantic similarity between generated medical reports and the ground truth. Our approach is validated, demonstrating its efficiency in assessing domain-specific semantic similarity within medical contexts. By applying our metric to state-of-the-art Chest X-ray report generation models, we obtain results that not only align with conventional metrics but also provide more contextually meaningful scores in the considered medical domain.