Evidence map›Paper›PMID 40615772›Full record

ArticleJournal of cancer education : the official journal of the American Association for Cancer Education2026

Accuracy and Reliability of ChatGPT in Answering Patient Questions About Lung Cancer and Its Surgery: An Expert Panel Evaluation by Thoracic Surgeons.

Onur Akçay, Özgür Öztürk, Tuba Acar, Soner Gürsoy

Abstract read
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In one paragraph

Article in Journal of cancer education : the official journal of the American Association for Cancer Education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Evaluating ChatGPT-4o and DeepSeek-R1 for Patient Education in Nasopharyngeal Carcinoma Radiotherapy: a Comparative Analysis.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026
    Article
  4. ChatGPT and Gemini in warfarin counseling.Croatian medical journal · 2026
    Article
  5. Article
  6. Article
  7. Article
  8. 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

4 authors.

Onur AkçayDepartment of Thoracic Surgery, Bakırcay University, Cigli Training and Research Hospital, Yeni Mahalle, Murat Karayalçin Bulvari No:18, 35620, Çiğli/İzmir, Turkey. onur_akcay@yahoo.com.ORCID 0000-0002-7233-2122
Özgür ÖztürkDepartment of Thoracic Surgery, Bakırcay University, Cigli Training and Research Hospital, Yeni Mahalle, Murat Karayalçin Bulvari No:18, 35620, Çiğli/İzmir, Turkey.
Tuba AcarDepartment of Thoracic Surgery, Bakırcay University, Cigli Training and Research Hospital, Yeni Mahalle, Murat Karayalçin Bulvari No:18, 35620, Çiğli/İzmir, Turkey.ORCID 0000-0003-3972-6485
Soner GürsoyDepartment of Thoracic Surgery, Bakırcay University, Cigli Training and Research Hospital, Yeni Mahalle, Murat Karayalçin Bulvari No:18, 35620, Çiğli/İzmir, Turkey.ORCID 0000-0001-7782-0742

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to evaluate the accuracy, clarity, and scientific adequacy of ChatGPT's responses to frequently asked patient questions concerning lung cancer and its surgical treatment, through an expert panel of thoracic surgeons. A total of 36 frequently asked questions-20 related to lung cancer and 16 related to lung cancer surgery-were collected from various online sources and clinical experience. These questions were submitted to ChatGPT-4.0 in a single session, and the initial responses were assessed by four experienced thoracic surgeons. Each response was scored independently using a 5-point Likert scale for scientific adequacy, clarity, and accuracy. The mean scores, standard deviations, and word counts were calculated. Inter-group comparisons were conducted using independent-samples t-tests. ChatGPT's responses were rated generally high across all domains. For lung cancer questions, the mean scores were 4.50 ± 0.18 (scientific adequacy), 4.57 ± 0.21 (clarity), and 4.66 ± 0.21 (accuracy), with an average word count of 152.4 ± 36.86. For surgical questions, scores were slightly higher: 4.57 ± 0.31, 4.64 ± 0.26, and 4.73 ± 0.21, respectively, with an average word count of 163.68 ± 35.64. Although the differences were not statistically significant, responses to surgical questions were associated with slightly higher agreement scores. Full scores were achieved in three surgical questions. ChatGPT demonstrated a high degree of reliability and clarity in answering commonly asked patient questions about lung cancer and surgery. While the model can serve as a supportive educational tool, it should not replace personalized physician-patient communication, especially in clinical decision-making processes.

Indexed as

Lung NeoplasmsSurgeonsFemaleGenerative Artificial IntelligenceHumansLarge Language ModelsPhysician-Patient RelationsReproducibility of ResultsSurveys and QuestionnairesArtificial intelligence in medicineChatGPT evaluationLung cancer education

Identifiers

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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.