Evidence map›Paper›PMID 42484768›Full record

ArticleJournal of robotic surgery2026

Reliability and readability of AI chatbot responses to patient questions about robot-assisted radical cystectomy.

Yang Liu, Rongkang Li, Peng Yu, Yi Zhang, Anguo Zhao, Xi Xiao, Zhilin Li, Rui Liang, Lei Peng, Zhilong Dong

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Journal of robotic surgery, 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
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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

10 authors.

Yang Liu *Department of Urology, The Second Hospital & Clinical Medical School, Lanzhou University, Gansu, China.
Rongkang Li *Department of Urology, The Second Hospital & Clinical Medical School, Lanzhou University, Gansu, China.
Peng Yu *Department of Urology, The Second Hospital & Clinical Medical School, Lanzhou University, Gansu, China.
Yi ZhangDepartment of Urology, The Second Hospital & Clinical Medical School, Lanzhou University, Gansu, China.
Anguo ZhaoDepartment of Urology, Medical School, South China Hospital, Shenzhen University, Shenzhen, Guangdong, China.
Xi XiaoDepartment of Urology, The Second Hospital & Clinical Medical School, Lanzhou University, Gansu, China.
Zhilin LiDepartment of Urology, Medical School, South China Hospital, Shenzhen University, Shenzhen, Guangdong, China.
Rui LiangDepartment of Urology, Medical School, South China Hospital, Shenzhen University, Shenzhen, Guangdong, China. 18326900957@163.com.
Lei PengDepartment of Urology, The Second Hospital & Clinical Medical School, Lanzhou University, Gansu, China. penglei933@163.com.
Zhilong DongDepartment of Urology, The Second Hospital & Clinical Medical School, Lanzhou University, Gansu, China. dzl19780829@163.com.

Funding

Cuiying Science and Technology Innovation Program CY2024-LC-A01Fundamental Research Funds for the Central Universities lzujbky-2023-ct06Gansu Province Key Talent Program 2023RCXM42Gansu Province Major Science and Technology Special Project 24ZDFA007Gansu Provincial Health Industry Research Project GSWSKY2022-63Key Incubation Project Funds of the Second Hospital & Clinical Medical School, Lanzhou University 2025-21-zdfy-009
6 · The paper itself

Abstract

Robot-assisted radical cystectomy (RARC) is a complex procedure that requires patients to understand surgical indications, urinary diversion, perioperative treatment, complications, recovery, and long-term functional outcomes. Although artificial intelligence (AI) chatbots are increasingly used to obtain medical information, their suitability for RARC patient education remains unclear. We conducted a cross-sectional comparative evaluation of four contemporary AI chatbots: ChatGPT-5, DeepSeek-V4, Claude Sonnet 4.6, and Gemini 3.5 Pro. A set of 20 core patient-education questions on RARC was developed by three senior urologic experts. Chatbot responses were assessed using DISCERN, the Ensuring Quality Information for Patients tool, the Global Quality Scale, and JAMA benchmark criteria. Readability was evaluated using the Automated Readability Index, Coleman-Liau Index, Flesch-Kincaid Grade Level, Flesch Reading Ease, Gunning Fog Index, and SMOG. Reliability scores differed significantly across models for DISCERN, EQIP, and GQS, while JAMA benchmark criteria were summarized descriptively as transparency signals. DeepSeek-V4 achieved the highest mean scores for DISCERN, EQIP, and GQS, while ChatGPT-5 and DeepSeek-V4 showed the strongest JAMA benchmark performance. Gemini 3.5 Pro generally had the lowest reliability and transparency scores. Readability also varied across models. DeepSeek-V4 produced the most readable responses overall, whereas Gemini 3.5 Pro generated the most complex text. However, all models exceeded the recommended sixth-grade reading level, and FRES scores remained below the recommended threshold. Contemporary AI chatbots generated responses with variable presentation quality, transparency, and readability for common RARC patient-education questions. Because factual accuracy was not directly assessed, these tools should not be interpreted as validated sources of clinical guidance and should not replace individualized counseling by urologists.

Indexed as

Artificial IntelligenceComprehensionCystectomyPatient Education as TopicRobotic Surgical ProceduresCross-Sectional StudiesHumansReproducibility of ResultsArtificial intelligenceChatbotInformation qualityPatient educationReadabilityRobot-assisted radical cystectomy

Identifiers

PMID42484768

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.