ArticleDigital health
Evaluation of the accuracy and quality of ChatGPT-4 responses for hyperparathyroidism patients discussed at multidisciplinary endocrinology meetings.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Gray-zone decisions in subclinical hypothyroidism: a qualitative analysis of ChatGPT responses.BMC endocrine disorders · 2026Article
- High accuracy but limited readability of large language model-generated responses to frequently asked questions about Kienböck's disease.BMC musculoskeletal disorders · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Chat Generative Pre-trained Transformer (ChatGPT) is now utilized in various fields of healthcare in order to obtain answers to questions related to healthcare-related problems and to evaluate available information. Primary hyperparathyroidism is a common endocrine disorder. We aimed to evaluate the accuracy and quality of ChatGPT's responses to questions specific to hyperparathyroidism cases discussed at multidisciplinary endocrinology meetings. Methods: ChatGPT-4 was asked to respond to 10 hyperparathyroidism cases evaluated at multidisciplinary endocrinology meetings. The accuracy, completeness, and quality of the responses were scored independently by two endocrinologists. Accuracy and completeness were evaluated on the Likert scale, and quality was evaluated on the global quality scale (GQS). Results: No misleading information was detected in the responses. In terms of diagnosis, the mean accuracy scores (ranging from 1 to 5) were 4.9 ± 0.1 and the mean completeness scores (ranging from 1 to 3) were 3.0. In the responses given in terms of further examination, the mean accuracy and completeness scores were 4.8 ± 0.13 and 2.6 ± 0.16, respectively. The mean accuracy and completeness scores for treatment recommendations were 4.9 ± 0.1 and 2.4 ± 0.16, respectively. The GQS evaluation result was 80% high quality and 20% medium quality. Conclusion: In this study, the accuracy and quality rates of ChatGPT-4 were generally high in responding to questions as to hyperparathyroidism patients. It can be concluded that artificial intelligence may serve as a valuable tool in healthcare. However, the limitations and risks of ChatGPT should also be evaluated.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.