Evidence map›Paper›PMID 39058491›Full record

ArticleJAMA network open2024

Readability and Information Quality in Cancer Information From a Free vs Paid Chatbot.

David Musheyev, Alexander Pan, Preston Gross, Daniel Kamyab, Peter Kaplinsky, Mark Spivak, Marie A Bragg, Stacy Loeb, Abdo E Kabarriti

Abstract read
In one paragraph

Article in JAMA network open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

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  14. Evidence-Based Analysis of AI Chatbots in Oncology Patient Education: Implications for Trust, Perceived Realness, and Misinformation Management.Journal of cancer education : the official journal of the American Association for Cancer Education · 2025
    Review
  15. Article
  16. Article
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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

9 authors.

David MusheyevDepartment of Urology, State University of New York Downstate Health Sciences University, New York.
Alexander PanDepartment of Urology, State University of New York Downstate Health Sciences University, New York.
Preston GrossDepartment of Urology, State University of New York Downstate Health Sciences University, New York.
Daniel KamyabDepartment of Urology, State University of New York Downstate Health Sciences University, New York.
Peter KaplinskyDepartment of Urology, State University of New York Downstate Health Sciences University, New York.
Mark SpivakDepartment of Urology, State University of New York Downstate Health Sciences University, New York.
Marie A BraggDepartment of Urology, New York University and Manhattan Veterans Affairs, New York.
Stacy LoebDepartment of Urology, New York University and Manhattan Veterans Affairs, New York.
Abdo E KabarritiDepartment of Urology, State University of New York Downstate Health Sciences University, New York.

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

Importance: The mainstream use of chatbots requires a thorough investigation of their readability and quality of information. Objective: To identify readability and quality differences in information between a free and paywalled chatbot cancer-related responses, and to explore if more precise prompting can mitigate any observed differences. Design, Setting, and Participants: This cross-sectional study compared readability and information quality of a chatbot's free vs paywalled responses with Google Trends' top 5 search queries associated with breast, lung, prostate, colorectal, and skin cancers from January 1, 2021, to January 1, 2023. Data were extracted from the search tracker, and responses were produced by free and paywalled ChatGPT. Data were analyzed from December 20, 2023, to January 15, 2024. Exposures: Free vs paywalled chatbot outputs with and without prompt: "Explain the following at a sixth grade reading level: [nonprompted input]." Main Outcomes and Measures: The primary outcome measured the readability of a chatbot's responses using Flesch Reading Ease scores (0 [graduate reading level] to 100 [easy fifth grade reading level]). Secondary outcomes included assessing consumer health information quality with the validated DISCERN instrument (overall score from 1 [low quality] to 5 [high quality]) for each response. Scores were compared between the 2 chatbot models with and without prompting. Results: This study evaluated 100 chatbot responses. Nonprompted free chatbot responses had lower readability (median [IQR] Flesh Reading ease scores, 52.60 [44.54-61.46]) than nonprompted paywalled chatbot responses (62.48 [54.83-68.40]) (P < .05). However, prompting the free chatbot to reword responses at a sixth grade reading level was associated with increased reading ease scores than the paywalled chatbot nonprompted responses (median [IQR], 71.55 [68.20-78.99]) (P < .001). Prompting was associated with increases in reading ease in both free (median [IQR], 71.55 [68.20-78.99]; P < .001)and paywalled versions (median [IQR], 75.64 [70.53-81.12]; P < .001). There was no significant difference in overall DISCERN scores between the chatbot models, with and without prompting. Conclusions and Relevance: In this cross-sectional study, paying for the chatbot was found to provide easier-to-read responses, but prompting the free version of the chatbot was associated with increased response readability without changing information quality. Educating the public on how to prompt chatbots may help promote equitable access to health information.

Indexed as

ComprehensionNeoplasmsConsumer Health InformationCross-Sectional StudiesHealth LiteracyHumansInternet

Identifiers

PMID39058491
PMCPMC11282443

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.