Article

Benchmarking the Performance of Large Language Models in Uveitis: A Comparative Analysis of ChatGPT-3.5, ChatGPT-4.0, Google Gemini, and Anthropic Claude3

2024-06-25

Abstract excerpt

<title>Abstract</title> <p>BACKGROUND/OBJECTIVE This study aimed to evaluate the accuracy, comprehensiveness, and readability of responses generated by various Large Language Models (LLMs) (ChatGPT-3.5, Gemini, Claude 3, and GPT-4.0) in the clinical context of uveitis, utilizing a meticulous grading methodology. METHODS Twenty-seven clinical uveitis questions were presented individually to four Large Language M...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 23578c49-5c96-553e-b066-25358fbbfedd
DOI: 10.21203/rs.3.rs-4237467/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Benchmarking the Performance of Large Language Models in Uveitis: A Comparative Analysis of ChatGPT-3.5, ChatGPT-4.0, Google Gemini, and Anthropic Claude3DOI 10.21203/rs.3.rs-4237467/v1Read

Can Large Language Models Provide High-Quality Information for Patients with Multidrug-Resistant Organism Infections: A Multidimensional Comparative Analysis of DeepSeek-V3, ChatGPT-5.2, Gemini-3, and Grok-4Semantic proximity Read

ChatGPT 3.5 and 4.0: A Head to Head Comparison in Generating Pharyngitis Patient GuidesSemantic proximity Read

How Far Have Large Language Models Advanced in Ophthalmology? A Systematic Review of Their Development, Evaluation, and Readiness for Clinical UseSemantic proximity Read

A Blinded Comparative Evaluation of Clinical and AI-Generated Responses to Otologic Patient QueriesSemantic proximity Read

The TRIPOD-LLM Statement: A Targeted Guideline For Reporting Large Language Models UseSemantic proximity Read

Comparative Performance of General-Purpose Large Language Models and a Specialized Clinical AI Tool on OKAP-Style Ophthalmology QuestionsSemantic proximity Read

How Far Have Large Language Models Advanced in Ophthalmology? A Systematic Review of Their Development, Evaluation, and Readiness for Clinical UseSemantic proximity Read

Diagnostic Performance of Large Language Models and Radiologists in Case-Based Radiology QuestionsSemantic proximity Read

ChatGPT Assisting Diagnosis of Neuro-ophthalmology Diseases Based on Case ReportsSemantic proximity Read

Divergence Between Clinical Accuracy and Readability in Large Language Models: A Comparative Study in HypogonadismSemantic proximity Read

Physician Evaluations of Large Language Model-Generated Responses to Medical Questions by Region and Years in Practice: A preliminary studySemantic proximity Read

Multidimensional Evaluation of Large Language Models on the AAP In-Service Examination: Assessing Accuracy, Calibration, and Citation ReliabilitySemantic proximity Read