A comparison of the diagnostic ability of large language models in challenging clinical cases

This research underscores the variation in model performance in clinical scenarios and highlights the importance of considering diagnostic model performance in diverse clinical scenarios prior to deployment. Furthermore, we provide a new tool to assess LLM output.

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Publication Details

Journal
Frontiers in Artificial Intelligence
Published
2024-08-05
DOI
https://doi.org/10.3389/frai.2024.1379297
Citations
10
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
1.20
Controls
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article

A comparison of the diagnostic ability of large language models in challenging clinical cases

Eoin Daniel O’Sullivan, Maria Palwasha Khan
10 citations
Frontiers in Artificial Intelligence
Artificial Intelligence in Healthcare and Education
1.20
article

A comparison of the diagnostic ability of large language models in challenging clinical cases

Eoin Daniel O’Sullivan, Maria Palwasha Khan
article en
10 citations

Abstract

This research underscores the variation in model performance in clinical scenarios and highlights the importance of considering diagnostic model performance in diverse clinical scenarios prior to deployment. Furthermore, we provide a new tool to assess LLM output.

Frontiers in Artificial IntelligenceVol. 7
The University of Queensland (AU)
Quality Education
Openalex Percentile: Top 20%
Artificial Intelligence in Healthcare and Education
1.20
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A comparison of the diagnostic ability of large language models in challenging clinical cases — Eoin Daniel O’Sullivan, Maria Palwasha Khan · Frontiers in Artificial Intelligence (2024) | TGRS Research Map | TGRS