From Text to Image: Exploring GPT-4Vision's Potential in Advanced Radiological Analysis across Subspecialties
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866908781894434816 |
|---|---|
| author | Busch, Felix Han, Tianyu Makowski, Marcus Truhn, Daniel Bressem, Keno Adams, Lisa |
| author_facet | Busch, Felix Han, Tianyu Makowski, Marcus Truhn, Daniel Bressem, Keno Adams, Lisa |
| contents | The study evaluates and compares GPT-4 and GPT-4Vision for radiological tasks, suggesting GPT-4Vision may recognize radiological features from images, thereby enhancing its diagnostic potential over text-based descriptions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_14777 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | From Text to Image: Exploring GPT-4Vision's Potential in Advanced Radiological Analysis across Subspecialties Busch, Felix Han, Tianyu Makowski, Marcus Truhn, Daniel Bressem, Keno Adams, Lisa Image and Video Processing Computer Vision and Pattern Recognition The study evaluates and compares GPT-4 and GPT-4Vision for radiological tasks, suggesting GPT-4Vision may recognize radiological features from images, thereby enhancing its diagnostic potential over text-based descriptions. |
| title | From Text to Image: Exploring GPT-4Vision's Potential in Advanced Radiological Analysis across Subspecialties |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2311.14777 |