Structured reporting may improve the radiological workflow and communication among physicians. Artificial intelligence applications in medicine are growing fast. Large language models (LLMs) are recently gaining importance as valuable tools in radiology and are currently being tested for the critical task of structured reporting. We compared four LLMs models in terms of knowledge on structured reporting and templates proposal. LLMs hold a great potential for generating structured reports in radiology but additional formal validations are needed on this topic.
Large language models for structured reporting in radiology: performance of GPT-4, ChatGPT-3.5, Perplexity and Bing
Mallio, Carlo A.
;Beomonte Zobel, Bruno
2023-01-01
Abstract
Structured reporting may improve the radiological workflow and communication among physicians. Artificial intelligence applications in medicine are growing fast. Large language models (LLMs) are recently gaining importance as valuable tools in radiology and are currently being tested for the critical task of structured reporting. We compared four LLMs models in terms of knowledge on structured reporting and templates proposal. LLMs hold a great potential for generating structured reports in radiology but additional formal validations are needed on this topic.File in questo prodotto:
File | Dimensione | Formato | |
---|---|---|---|
20.500.12610-79864.pdf
non disponibili
Tipologia:
Versione Editoriale (PDF)
Licenza:
Copyright dell'editore
Dimensione
2.19 MB
Formato
Adobe PDF
|
2.19 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.