Evidence map›Paper›PMID 42518148›Full record

ArticleAnnals of surgical oncology2026

Comparative Evaluation of AI Chatbots for Testicular Cancer Education: Validity, Information Quality, and Readability.

Jiadong Zhao, Shuolei Sun, Anguo Zhao, Rui Liang, Lei Peng, Xiqi Peng, Rongkang Li

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Annals of surgical oncology, 2026. 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

7 authors.

Jiadong Zhao *Department of Urology, South China Hospital, Medical School, Shenzhen University, Shenzhen, China.
Shuolei Sun *Department of Urology, Peking University Shenzhen Hospital, Shenzhen, China.
Anguo ZhaoDepartment of Urology, South China Hospital, Medical School, Shenzhen University, Shenzhen, China.
Rui LiangDepartment of Urology, South China Hospital, Medical School, Shenzhen University, Shenzhen, China.
Lei PengDepartment of Urology, South China Hospital, Medical School, Shenzhen University, Shenzhen, China.
Xiqi PengDepartment of Urology, South China Hospital, Medical School, Shenzhen University, Shenzhen, China. xqpeng@szu.edu.cn.
Rongkang LiDepartment of Urology, South China Hospital, Medical School, Shenzhen University, Shenzhen, China. lirongkanghao@163.com.

Funding

Shenzhen Medical Research Fund A2502022Shenzhen Medical Research Fund A2503070Shenzhen Municipal Science and Technology Innovation Council JCYJ20240813144023031
6 · The paper itself

Abstract

backgroundLarge language models (LLMs) are increasingly used in health information seeking, but their performance in testicular cancer education remains unclear. This study evaluated the validity, reliability, and readability of responses generated by four widely used artificial intelligence (AI) chatbots.

methodsFour AI chatbots (ChatGPT 5.2, Copilot 2025, DeepSeek V3.2, and Gemini 2.5 Pro) were assessed. Structured clinical-knowledge accuracy was evaluated using 250 testicular cancer-related multiple choice questions across five domains, each administered to each model three times. The information quality of patient-facing responses was assessed using 12 patient-oriented questions derived from Google Trends queries and refined based on clinical experience, with responses rated using DISCERN, Ensuring Quality Information for Patients (EQIP), Global Quality Scale (GQS), and Journal of the American Medical Association (JAMA) benchmark criteria. Readability was measured using six standard readability indices and compared with the sixth-grade level recommended by the American Medical Association and the National Institutes of Health.

resultsAll four chatbots demonstrated high structured clinical-knowledge accuracy, with overall multiple choice question accuracy exceeding 96%. ChatGPT 5.2 achieved the highest accuracy (98.40%), followed by Gemini 2.5 Pro (97.20%), DeepSeek V3.2 (97.07%), and Copilot 2025 (96.93%). Overall accuracy differed significantly among models (p = 0.026), and only ChatGPT 5.2 significantly outperformed Gemini 2.5 Pro after Bonferroni correction. ChatGPT 5.2 also achieved the highest information-quality scores on DISCERN, EQIP, and GQS, whereas JAMA scores were uniformly low across all models. DeepSeek V3.2 generated the most readable responses overall. However, none of the chatbots met recommended readability thresholds, and all significantly deviated from sixth-grade readability benchmarks.

conclusionAI chatbots showed high validity in answering testicular cancer-related questions, but important differences remained in information quality and readability. ChatGPT 5.2 provided the most reliable responses, whereas DeepSeek V3.2 produced the most readable content. These tools may support patient education, but they cannot yet replace clinician-guided and evidence-based communication.

Indexed as

Artificial IntelligenceComprehensionHealth LiteracyPatient Education as TopicTesticular NeoplasmsHumansLarge Language ModelsMaleReproducibility of ResultsSurveys and QuestionnairesArtificial intelligenceChatbotsInformation qualityReadabilityTesticular cancer

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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.