Assessing GPT-4 multimodal performance in radiological image analysis
GPT-4V's capability in analyzing images offers new clinical possibilities in radiology. GPT-4V excels in identifying imaging modalities but demonstrates inconsistent anatomy and pathology detection. Ongoing AI advancements are necessary to enhance diagnostic reliability in radiological applications.
Authors
- Vera Sorin (ORCID: https://orcid.org/0000-0003-0509-4686)
- Benjamin Scott Glicksberg (ORCID: https://orcid.org/0000-0003-4515-8090)
- Eyal Klang (ORCID: https://orcid.org/0000-0002-4567-3108)
- Girish N. Nadkarni (ORCID: https://orcid.org/0000-0001-6319-4314)
- Eli Konen (ORCID: https://orcid.org/0000-0001-9507-2450)
- Yiftach Barash (ORCID: https://orcid.org/0000-0002-7242-1328)
- Dana Brin (ORCID: https://orcid.org/0009-0003-7316-206X)
Institutions
- Tel Aviv University (IL)
- Sheba Medical Center (IL)
- Academic College of Tel Aviv-Yafo (IL)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- European Radiology
- Published
- 2024-08-30
- DOI
- https://doi.org/10.1007/s00330-024-11035-5
- Citations
- 66
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 26.68