Evidence map›Paper›PMID 39816417›Full record

ArticleSisli Etfal Hastanesi tip bulteni2024

Evaluation of ChatGPT-4 Performance in Answering Patients' Questions About the Management of Type 2 Diabetes.

Puren Gokbulut, Serife Mehlika Kuskonmaz, Cagatay Emir Onder, Isilay Taskaldiran, Gonul Koc

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Article in Sisli Etfal Hastanesi tip bulteni, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Puren GokbulutDepartment of Endocrinology and Metabolic Diseases, Ankara Training and Research Hospital, Ankara, Türkiye.
Serife Mehlika KuskonmazDepartment of Endocrinology and Metabolic Diseases, Ankara Training and Research Hospital, Ankara, Türkiye.
Cagatay Emir OnderDepartment of Endocrinology and Metabolic Diseases, Ankara Training and Research Hospital, Ankara, Türkiye.
Isilay TaskaldiranDepartment of Endocrinology and Metabolic Diseases, Ankara Training and Research Hospital, Ankara, Türkiye.
Gonul KocDepartment of Endocrinology and Metabolic Diseases, Ankara Training and Research Hospital, Ankara, Türkiye.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: Type 2 diabetes mellitus is a disease with a rising prevalence worldwide. Person-centered treatment factors, including comorbidities and treatment goals, should be considered in determining the pharmacological treatment of type 2 diabetes. ChatGPT-4 (Generative Pre-trained Transformer), a large language model, holds the potential performance in various fields, including medicine. We aimed to examine the reliability, quality, reproducibility, and readability of ChatGPT-4's responses to clinical scenarios about the medical treatment approach and management of type 2 diabetes patients. Methods: ChatGPT-4's responses to 24 questions were independently graded by two endocrinologists with clinical experience in endocrinology and resolved by a third reviewer based on the ADA(American Diabetes Association) 2023 guidelines. DISCERN (Quality Criteria for Consumer Health Information) Measurement Tool was used to evaluate the reliability and quality of information. Results: Responses to questions by ChatGPT-4 were fairly consistent in both sessions. No false or misleading information was found in any ChatGPT-4 responses. In terms of reliability, most of the answers showed good (87.5%), followed by excellent (12.5%) reliability. Reading Level was classified as fairly difficult to read (8.3%), difficult to read (50%), and very difficult to read (41.7%). Conclusion: ChatGPT-4 may have a role as an additional informative tool for type 2 diabetes patients for medical treatment approaches.

Indexed as

Artificial intelligenceChatGPT-4medical treatmenttype 2 diabetes mellitus

Identifiers

PMID39816417
PMCPMC11729837

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.