ChatGPT's Epoch in Rheumatological Diagnostics: A Critical Assessment in the Context of Sjögren's Syndrome.

Document Type

Article

Publication Date

10-26-2023

Publication Title

Cureus

Abstract

INTRODUCTION: The rise of artificial intelligence in medical practice is reshaping clinical care. Large language models (LLMs) like ChatGPT have the potential to assist in rheumatology by personalizing scientific information retrieval, particularly in the context of Sjögren's Syndrome. This study aimed to evaluate the efficacy of ChatGPT in providing insights into Sjögren's Syndrome, differentiating it from other rheumatological conditions.

MATERIALS AND METHODS: A database of peer-reviewed articles and clinical guidelines focused on Sjögren's Syndrome was compiled. Clinically relevant questions were presented to ChatGPT, with responses assessed for accuracy, relevance, and comprehensiveness. Techniques such as blinding, random control queries, and temporal analysis ensured unbiased evaluation. ChatGPT's responses were also assessed using the 15-questionnaire DISCERN tool.

RESULTS: ChatGPT effectively highlighted key immunopathological and histopathological characteristics of Sjögren's Syndrome, though some crucial data and citation inconsistencies were noted. For a given clinical vignette, ChatGPT correctly identified potential etiological considerations with Sjögren's Syndrome being prominent.

DISCUSSION: LLMs like ChatGPT offer rapid access to vast amounts of data, beneficial for both patients and providers. While it democratizes information, limitations like potential oversimplification and reference inaccuracies were observed. The balance between LLM insights and clinical judgment, as well as continuous model refinement, is crucial.

CONCLUSION: LLMs like ChatGPT offer significant potential in rheumatology, providing swift and broad medical insights. However, a cautious approach is vital, ensuring rigorous training and ethical application for optimal patient care and clinical practice.

Volume

15

Issue

10

First Page

e47754

DOI

10.7759/cureus.47754

ISSN

2168-8184

PubMed ID

38022092

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