Summary of Reshaping Free-text Radiology Notes Into Structured Reports with Generative Transformers, by Laura Bergomi et al.
Reshaping Free-Text Radiology Notes Into Structured Reports With Generative Transformers
by Laura Bergomi, Tommaso M. Buonocore, Paolo Antonazzo, Lorenzo Alberghi, Riccardo Bellazzi, Lorenzo Preda, Chandra Bortolotto, Enea Parimbelli
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a pipeline to extract clinical information from free-text radiology reports using Natural Language Processing (NLP) and Transformer-based models. The goal is to standardize radiology reporting by automatically filling out structured reporting registry items for CT staging of lymphoma patients. The authors leverage the IT5 model, a domain-specific version of T5, with two strategies to overcome context length limitations. Performance is evaluated using strict accuracy, F1, and format accuracy metrics, which are compared to GPT-3.5 Large Language Model results. Human-expert feedback scores show high correlation with AI performance metrics and confirm the superior ability of larger language models in generating human-like statements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper helps computers understand radiology reports that doctors write by hand. These reports are hard to read because they’re not organized or standardized. The team created a system using special computer programs (Natural Language Processing) and algorithms (Transformer-based models) to automatically fill out the standard report templates. They tested their method on 174 reports and compared it to another popular computer program (GPT-3.5). Human doctors evaluated the results, saying that larger language models can generate statements that sound like what a doctor would write. |
Keywords
» Artificial intelligence » Context length » Gpt » Large language model » Natural language processing » Nlp » T5 » Transformer
