Enhancing the CAD-RADS™ 2.0 Category Assignment Performance of ChatGPT and DeepSeek Through “Few-shot” Prompting

Few-shot prompting substantially enhances LLMs' accuracy in assigning CAD-RADS™ 2.0 categories, suggesting potential for clinical application and facilitating system adoption.

Authors

Institutions

Publication Details

Journal
Journal of Computer Assisted Tomography
Published
2025-09-23
DOI
https://doi.org/10.1097/rct.0000000000001802
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Enhancing the CAD-RADS™ 2.0 Category Assignment Performance of ChatGPT and DeepSeek Through “Few-shot” Prompting

Hasan Emin Kaya
Journal of Computer Assisted Tomography
Artificial Intelligence in Healthcare and Education
article

Enhancing the CAD-RADS™ 2.0 Category Assignment Performance of ChatGPT and DeepSeek Through “Few-shot” Prompting

Hasan Emin Kaya
article en

Abstract

Few-shot prompting substantially enhances LLMs' accuracy in assigning CAD-RADS™ 2.0 categories, suggesting potential for clinical application and facilitating system adoption.

Journal of Computer Assisted TomographyVol. 50(2)
Bursa Uludağ Üni̇versi̇tesi̇ (TR)
Openalex Percentile: Top 81%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.