Evidence map›Paper›PMID 40228277›Full record

Observational studyMedicine2025

Assessing the quality and readability of patient education materials on chemotherapy cardiotoxicity from artificial intelligence chatbots: An observational cross-sectional study.

Christoph A Stephenson-Moe, Benjamin J Behers, Rebecca M Gibons, Brett M Behers, Laura De Jesus Herrera, Djhemson Anneaud, Manuel A Rosario, Caroline N Wojtas, Samantha Bhambrah, Karen M Hamad

Abstract readObservational Study
In one paragraph

Observational study in Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 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

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.

Christoph A Stephenson-MoeFlorida State University College of Medicine, Tallahassee, FL.ORCID 0009-0004-1003-3444
Benjamin J BehersFlorida State University Internal Medicine Residency, Sarasota Memorial Hospital, Sarasota, FL.
Rebecca M GibonsFlorida State University College of Medicine, Tallahassee, FL.
Brett M BehersUniversity of South Florida College of Medicine, Tampa, FL.
Laura De Jesus HerreraFlorida State University Internal Medicine Residency, Sarasota Memorial Hospital, Sarasota, FL.
Djhemson AnneaudFlorida State University Internal Medicine Residency, Sarasota Memorial Hospital, Sarasota, FL.
Manuel A RosarioFlorida State University Internal Medicine Residency, Sarasota Memorial Hospital, Sarasota, FL.
Caroline N WojtasFlorida State University Internal Medicine Residency, Sarasota Memorial Hospital, Sarasota, FL.
Samantha BhambrahFlorida State University Internal Medicine Residency, Sarasota Memorial Hospital, Sarasota, FL.
Karen M HamadFlorida State University Internal Medicine Residency, Sarasota Memorial Hospital, Sarasota, FL.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) and the introduction of Large Language Model (LLM) chatbots have become a common source of patient inquiry in healthcare. The quality and readability of AI-generated patient education materials (PEM) is the subject of many studies across multiple medical topics. Most demonstrate poor readability and acceptable quality. However, an area yet to be investigated is chemotherapy-induced cardiotoxicity. This study seeks to assess the quality and readability of chatbot created PEM relative to chemotherapy-induced cardiotoxicity. We conducted an observational cross-sectional study in August 2024 by asking 10 questions to 4 chatbots: ChatGPT, Microsoft Copilot (Copilot), Google Gemini (Gemini), and Meta AI (Meta). The generated material was assessed for readability using 7 tools: Flesch Reading Ease Score (FRES), Flesch-Kincaid Grade Level (FKGL), Gunning Fog Index (GFI), Coleman-Liau Index (CLI), Simple Measure of Gobbledygook (SMOG) Index, Automated Readability Index (ARI), and FORCAST Grade Level. Quality was assessed using modified versions of 2 validated tools: the Patient Education Materials Assessment Tool (PEMAT), which outputs a 0% to 100% score, and DISCERN, a 1 (unsatisfactory) to 5 (highly satisfactory) scoring system. Descriptive statistics were used to evaluate performance and compare chatbots amongst each other. Mean reading grade level (RGL) across all chatbots was 13.7. Calculated RGLs for ChatGPT, Copilot, Gemini and Meta were 14.2, 14.0, 12.5, 14.2, respectively. Mean DISCERN scores across the chatbots was 4.2. DISCERN scores for ChatGPT, Copilot, Gemini, and Meta were 4.2, 4.3, 4.2, and 3.9, respectively. Median PEMAT scores for understandability and actionability were 91.7% and 75%, respectively. Understandability and actionability scores for ChatGPT, Copilot, Gemini, and Meta were 100% and 75%, 91.7% and 75%, 90.9% and 75%, and 91.7% and 50%, respectively. AI chatbots produce high quality PEM with poor readability. We do not discourage using chatbots to create PEM but recommend cautioning patients about their readability concerns. AI chatbots are not an alternative to a healthcare provider. Furthermore, there is no consensus on which chatbots create the highest quality PEM. Future studies are needed to assess the effectiveness of AI chatbots in providing PEM to patients and how the capabilities of AI chatbots are changing over time.

Indexed as

Antineoplastic AgentsArtificial IntelligenceCardiotoxicityComprehensionPatient Education as TopicCross-Sectional StudiesGenerative Artificial IntelligenceHealth LiteracyHumansAntineoplastic Agentsartificial intelligencechatbotschemotherapy cardiotoxicitypatient education materials

Identifiers

PMID40228277
PMCPMC11999455

What OpenQuestion holds

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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.