Evidence map›Paper›PMID 37428117›Full record

ArticleThe Journal of urology2023

Evaluating the Effectiveness of Artificial Intelligence-powered Large Language Models Application in Disseminating Appropriate and Readable Health Information in Urology.

Ryan Davis, Michael Eppler, Oluwatobiloba Ayo-Ajibola, Jeffrey C Loh-Doyle, Jamal Nabhani, Mary Samplaski, Inderbir Gill, Giovanni E Cacciamani

Abstract read
PubMed Publisher
In one paragraph

Article in The Journal of urology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
54citing papers in PubMed, 1 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

54 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  3. An expert-based assessment of the quality, reliability, understandability, and readability of ChatGPT responses on cerebral palsy.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Ryan DavisUSC Institute of Urology, and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California.ORCID 0009-0002-0408-8380
Michael EpplerUSC Institute of Urology, and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California.ORCID 0000-0001-6336-5857
Oluwatobiloba Ayo-AjibolaUSC Institute of Urology, and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California.
Jeffrey C Loh-DoyleUSC Institute of Urology, and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California.ORCID 0000-0002-7094-482X
Jamal NabhaniUSC Institute of Urology, and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California.
Mary SamplaskiUSC Institute of Urology, and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California.
Inderbir GillUSC Institute of Urology, and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California.
Giovanni E CacciamaniUSC Institute of Urology, and Catherine and Joseph Aresty Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, California.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe Internet is a ubiquitous source of medical information, and natural language processors are gaining popularity as alternatives to traditional search engines. However, suitability of their generated content for patients is not well understood. We aimed to evaluate the appropriateness and readability of natural language processor-generated responses to urology-related medical inquiries. MATERIALS AND

methodsEighteen patient questions were developed based on Google Trends and were used as inputs in ChatGPT. Three categories were assessed: oncologic, benign, and emergency. Questions in each category were either treatment or sign/symptom-related questions. Three native English-speaking Board-Certified urologists independently assessed appropriateness of ChatGPT outputs for patient counseling using accuracy, comprehensiveness, and clarity as proxies for appropriateness. Readability was assessed using the Flesch Reading Ease and Flesh-Kincaid Reading Grade Level formulas. Additional measures were created based on validated tools and assessed by 3 independent reviewers.

resultsFourteen of 18 (77.8%) responses were deemed appropriate, with clarity having the most 4 and 5 scores (

conclusionsDespite impressive capabilities, natural language processors have limitations as sources of medical information. Refinement is crucial before adoption for this purpose.

Indexed as

Health LiteracyUrologyArtificial IntelligenceComprehensionHumansInternetLanguageartificial intelligencecommunicationhealthsigns and symptomstherapeuticsurology

Identifiers

PMID37428117

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.