Assessing the Accuracy of Diagnostic Capabilities of Large Language Models
<b>Background:</b> In recent years, numerous artificial intelligence applications, especially generative large language models, have evolved in the medical field. This study conducted a structured comparative analysis of four leading generative large language models (LLMs)—ChatGPT-4o (Op...
Saved in:
| Main Authors: | Andrada Elena Urda-Cîmpean, Daniel-Corneliu Leucuța, Cristina Drugan, Alina-Gabriela Duțu, Tudor Călinici, Tudor Drugan |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/15/13/1657 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Risk of Bias Assessment of Diagnostic Accuracy Studies Using QUADAS 2 by Large Language Models
by: Daniel-Corneliu Leucuța, et al.
Published: (2025-06-01) -
Performance of the Large Language Models in African rheumatology: a diagnostic test accuracy study of ChatGPT-4, Gemini, Copilot, and Claude artificial intelligence
by: Yannick Laurent Tchenadoyo Bayala, et al.
Published: (2025-05-01) -
Comparative analysis of large language models on rare disease identification
by: Guangyu Ao, et al.
Published: (2025-04-01) -
Diagnostic efficacy of large language models in the pediatric emergency department: a pilot study
by: Francesco Del Monte, et al.
Published: (2025-07-01) -
Correction: Diagnostic efficacy of large language models in the pediatric emergency department: a pilot study
by: Francesco Del Monte, et al.
Published: (2025-07-01)