Evidence map›Paper›PMID 40405178›Full record

ArticleBMC medical education2025

Challenging cases of hyponatremia incorrectly interpreted by ChatGPT.

Kenrick Berend, Ashley Duits, Reinold O B Gans

Abstract readCase Reports
In one paragraph

Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

3 authors.

Kenrick BerendDepartment of Medicine, Curaçao Medical Center, Willemstad, Curaçao. kenber2@me.com.
Ashley DuitsInstitute for Medical Education, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
Reinold O B GansDepartment of Medicine, Curaçao Medical Center, Willemstad, Curaçao.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn clinical medicine, the assessment of hyponatremia is frequently required but also known as a source of major diagnostic errors, substantial mismanagement, and iatrogenic morbidity. Because artificial intelligence techniques are efficient in analyzing complex problems, their use may possibly overcome current assessment limitations. There is no literature concerning Chat Generative Pre-trained Transformer (ChatGPT-3.5) use for evaluating difficult hyponatremia cases. Because of the interesting pathophysiology, hyponatremia cases are often used in medical education for students to evaluate patients with students increasingly using artificial intelligence as a diagnostic tool. To evaluate this possibility, four challenging hyponatremia cases published previously, were presented to the free ChatGPT-3.5 for diagnosis and treatment suggestions.

methodsWe used four challenging hyponatremia cases, that were evaluated by 46 physicians in Canada, the Netherlands, South-Africa, Taiwan, and USA, and published previously. These four cases were presented two times in the free ChatGPT, version 3.5 in December 2023 as well as in September 2024 with the request to recommend diagnosis and therapy. Responses by ChatGPT were compared with those of the clinicians.

resultsCase 1 and 3 have a single cause of hyponatremia. Case 2 and 4 have two contributing hyponatremia features. Neither ChatGPT, in 2023, nor the previously published assessment by 46 clinicians, whose assessment was described in the original publication, recognized the most crucial cause of hyponatremia with major therapeutic consequences in all four cases. In 2024 ChatGPT properly diagnosed and suggested adequate management in one case. Concurrent Addison's disease was correctly recognized in case 1 by ChatGPT in 2023 and 2024, whereas 81% of the clinicians missed this diagnosis. No proper therapeutic recommendations were given by ChatGPT in 2023 in any of the four cases, but in one case adequate advice was given by ChatGPT in 2024. The 46 clinicians recommended inadequate therapy in 65%, 57%, 2%, and 76%, respectively in case 1 to 4.

conclusionOur study currently does not support the use of the free version ChatGPT 3.5 in difficult hyponatremia cases, but a small improvement was observed after ten months with the same ChatGPT 3.5 version. Patients, health professionals, medical educators and students should be aware of the shortcomings of diagnosis and therapy suggestions by ChatGPT.

Indexed as

Artificial IntelligenceDiagnostic ErrorsEducation, MedicalHyponatremiaGenerative Artificial IntelligenceHumansEcstasyHyponatremiaLow osmol intakeMineralocorticoid deficiencySIADH

Identifiers

PMID40405178
PMCPMC12100905

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.