Evidence map›Paper›PMID 42748159›Full record

ArticlePloS one2026

Comparative evaluation of ChatGPT and gemini responses to patient-oriented questions on breast cancer.

Yeliz Yilmaz Bozok, Nihan Acar, Murat Kemal Atahan, Cem Karaali

Abstract readComparative Study
In one paragraph

Article in PloS one, 2026. 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

4 authors.

Yeliz Yilmaz BozokDepartment of General Surgery, İzmir Atatürk Training and Research Hospital, İzmir Katip Çelebi University, İzmir, Turkey.ORCID https://orcid.org/0000-0003-1811-122X
Nihan AcarDepartment of General Surgery, İzmir Atatürk Training and Research Hospital, İzmir Katip Çelebi University, İzmir, Turkey.
Murat Kemal AtahanDepartment of General Surgery, İzmir Atatürk Training and Research Hospital, İzmir Katip Çelebi University, İzmir, Turkey.
Cem KaraaliDepartment of General Surgery, İzmir Atatürk Training and Research Hospital, İzmir Katip Çelebi University, İzmir, Turkey.ORCID https://orcid.org/0000-0003-2743-7360

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBreast cancer, the most common malignancy among women, remains a major global public health concern. With the rapid growth of artificial intelligence-based language models, it is essential to evaluate their potential roles in patient education. This study compared ChatGPT and Gemini in responding to patient-oriented questions about breast cancer and its surgical treatment regarding scientific accuracy, clarity, and unnecessary detail.

methodsForty frequently asked questions were collected from Turkish online sources. Both models were queried under identical conditions on August 13, 2025, and the responses were anonymized for blinded evaluation. Four general surgeons experienced in breast surgery independently assessed each response using a five-point Likert scale across three domains: scientific accuracy, clarity, and unnecessary detail.

resultsResponse lengths were recorded and compared. ChatGPT achieved significantly higher median scores than Gemini in scientific accuracy [4.75 (3.50-5.00) vs. 4.25 (3.50-5.00); p < 0.001], clarity [4.75 (3.50-5.00) vs. 4.25 (3.25-5.00); p = 0.005], and unnecessary detail [5.00 (5.00-5.00) vs. 4.50 (3.50-5.00); p < 0.001]. The overall median score was 4.83 (4.08-5.00) for ChatGPT and 4.33 (3.50-4.92) for Gemini (p < 0.001). Gemini's responses were significantly longer (244 ± 84 vs. 170 ± 47 words; p < 0.001).

conclusionBoth models demonstrated acceptable overall performance. However, ChatGPT's higher scores and shorter, more focused responses indicate that it may be a more efficient tool for addressing breast cancer-related patient questions. In their current form, these models should not be used for diagnostic or clinical decision-making purposes. AI-generated health information should be used only under expert supervision and for educational or supportive purposes.

Indexed as

Breast NeoplasmsPatient Education as TopicFemaleGenerative Artificial IntelligenceHumansLarge Language ModelsSurveys and Questionnaires

Identifiers

PMID42748159
PMCPMC13580998

What OpenQuestion holds

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Registered trials

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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.