Evaluating Microsoft Bing with ChatGPT-4 for the assessment of abdominal computed tomography and magnetic resonance images

The contribution of large language models (LLMs) to the diagnostic process in radiology is still being explored. However, with a comprehensive understanding of their capabilities and limitations, LLMs can significantly support radiologists during diagnosis and improve the overall efficiency of abdominal radiology practices. Acknowledging the limitations of current studies related to ChatGPT in this field, our work provides a foundation for future clinical research, paving the way for more integrated and effective diagnostic tools.

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Publication Details

Journal
Diagnostic and Interventional Radiology
Published
2024-08-19
DOI
https://doi.org/10.4274/dir.2024.232680
Citations
10
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
4.04
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article

Evaluating Microsoft Bing with ChatGPT-4 for the assessment of abdominal computed tomography and magnetic resonance images

Duygu Doğa Ekizalioğlu, Ezgi Güler, Alperen Elek
10 citations
Diagnostic and Interventional Radiology
Radiomics and Machine Learning in Medical Imaging
4.04
article

Evaluating Microsoft Bing with ChatGPT-4 for the assessment of abdominal computed tomography and magnetic resonance images

Duygu Doğa Ekizalioğlu, Ezgi Güler, Alperen Elek
article en
10 citations

Abstract

The contribution of large language models (LLMs) to the diagnostic process in radiology is still being explored. However, with a comprehensive understanding of their capabilities and limitations, LLMs can significantly support radiologists during diagnosis and improve the overall efficiency of abdominal radiology practices. Acknowledging the limitations of current studies related to ChatGPT in this field, our work provides a foundation for future clinical research, paving the way for more integrated and effective diagnostic tools.

Diagnostic and Interventional RadiologyVol. 31(3)
Ege University (TR)
Openalex Percentile: Top 6%
Radiomics and Machine Learning in Medical Imaging
4.04
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