Evidence map›Paper›PMID 37567827›Full record

ArticleEuropean urology2024

How Well Do Artificial Intelligence Chatbots Respond to the Top Search Queries About Urological Malignancies?

David Musheyev, Alexander Pan, Stacy Loeb, Abdo E Kabarriti

Abstract read
PubMed Publisher
In one paragraph

Article in European urology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
69citing papers in PubMed, 2 pooled it
–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

69 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  14. 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
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9 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

David MusheyevDepartment of Urology, State University of New York Downstate Health Sciences University, New York, NY, USA.
Alexander PanDepartment of Urology, State University of New York Downstate Health Sciences University, New York, NY, USA.
Stacy LoebDepartment of Urology, New York University and Manhattan Veterans Affairs, New York, NY, USA; Department of Population Health, New York University, New York, NY, USA.
Abdo E KabarritiDepartment of Urology, State University of New York Downstate Health Sciences University, New York, NY, USA. Electronic address: abdo.kabarriti@downstate.edu.

Funding

Addressing Misinformation to Promote Equity in Prostate Cancer Care - Revision - 1R01CA278997 · NCI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI Stacy Loeb · 2023 to 2026
$2.4M
NCI NIH HHS R01 CA278997
6 · The paper itself

Abstract

Artificial intelligence (AI) chatbots are becoming a popular source of information but there are limited data on the quality of information on urological malignancies that they provide. Our objective was to characterize the quality of information and detect misinformation about prostate, bladder, kidney, and testicular cancers from four AI chatbots: ChatGPT, Perplexity, Chat Sonic, and Microsoft Bing AI. We used the top five search queries related to prostate, bladder, kidney, and testicular cancers according to Google Trends from January 2021 to January 2023 and input them into the AI chatbots. Responses were evaluated for quality, understandability, actionability, misinformation, and readability using published instruments. AI chatbot responses had moderate to high information quality (median DISCERN score 4 out of 5, range 2-5) and lacked misinformation. Understandability was moderate (median Patient Education Material Assessment Tool for Printable Materials [PEMAT-P] understandability 66.7%, range 44.4-90.9%) and actionability was moderate to poor (median PEMAT-P actionability 40%, range 0-40%The responses were written at a fairly difficult reading level. AI chatbots produce information that is generally accurate and of moderate to high quality in response to the top urological malignancy-related search queries, but the responses lack clear, actionable instructions and exceed the reading level recommended for consumer health information. PATIENT SUMMARY: Artificial intelligence chatbots produce information that is generally accurate and of moderately high quality in response to popular Google searches about urological cancers. However, their responses are fairly difficult to read, are moderately hard to understand, and lack clear instructions for users to act on.

Indexed as

Testicular NeoplasmsUrologic NeoplasmsArtificial IntelligenceHumansMaleNeoplasms, Germ Cell and EmbryonalSoftwareArtificial intelligenceMisinformationUrological malignancies

Identifiers

PMID37567827

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.