Evidence map›Paper›PMID 41028089›Full record

ArticleScientific reports2025

Assessment of the validity of ChatGPT-3.5 responses to patient-generated queries following BPH surgery.

Jad Najdi, Bilal Alameddine, Alexandre Armache, Marwan Zein, William S Azar, Albert El-Hajj

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Jad Najdi *Department of Surgery, Division of Urology, American University of Beirut Medical Center, Beirut, Lebanon.ORCID http://orcid.org/0000-0001-6224-894X
Bilal Alameddine *Department of Surgery, Division of Urology, American University of Beirut Medical Center, Beirut, Lebanon.ORCID http://orcid.org/0009-0003-1070-5637
Alexandre ArmacheDepartment of Surgery, Division of Urology, American University of Beirut Medical Center, Beirut, Lebanon.ORCID http://orcid.org/0000-0003-1093-4539
Marwan ZeinDepartment of Surgery, Division of Urology, American University of Beirut Medical Center, Beirut, Lebanon.ORCID http://orcid.org/0000-0002-3188-9899
William S AzarDistrict of Columbia, Georgetown University School of Medicine, Washington, USA.ORCID http://orcid.org/0000-0003-0682-3465
Albert El-HajjDepartment of Surgery, Division of Urology, American University of Beirut Medical Center, Beirut, Lebanon. ae67@aub.edu.lb.ORCID http://orcid.org/0000-0002-0043-7195

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid advancement of artificial intelligence, particularly large language models like ChatGPT-3.5, presents promising applications in healthcare. This study evaluates ChatGPT-3.5's validity in responding to post-operative patient inquiries following surgery for benign prostatic hyperplasia (BPH). Common patient-generated questions were sourced from discharge instructions, online forums, and social media, covering various BPH surgical modalities. ChatGPT-3.5 responses were assessed by two senior urology residents using pre-defined criteria, with discrepancies resolved by a third reviewer. A total of 496 responses were reviewed, with 280 excluded. Among the 216 graded responses, 78.2% were comprehensive and correct, 9.3% were incomplete or partially correct, 10.2% contained a mix of accurate and inaccurate information, and 2.3% were entirely incorrect. Newer procedures (Aquablation, Rezum, iTIND) had a higher percentage of correct answers compared to traditional techniques (TURP, simple prostatectomy). The most common errors involved missing context or incorrect details (36.6%). These findings suggest that ChatGPT-3.5 has potential in providing accurate post-operative guidance for BPH patients. However, concerns regarding incomplete and misleading responses highlight the need for further refinement to improve AI-generated medical advice and ensure patient safety. Future research should focus on enhancing AI reliability in clinical applications.

Indexed as

Artificial IntelligenceProstatic HyperplasiaAgedGenerative Artificial IntelligenceHumansMaleProstatectomyReproducibility of ResultsSurveys and QuestionnairesArtificial intelligenceMinimally invasive surgical proceduresNatural language processingPostoperative careProstatic hyperplasia

Identifiers

PMID41028089
PMCPMC12484858

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.