Evidence map›Paper›PMID 42844537›Full record

ArticleCardiology and therapy2026

A Comparative Evaluation of ChatGPT and Google Search Output for Caregiver Education in Tetralogy of Fallot.

Shreyas V Menon, Armaun D Rouhi, Jason J Han

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Article in Cardiology and therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Shreyas V MenonCooper Medical School of Rowan University, Camden, NJ, USA.
Armaun D RouhiWashington University in St. Louis School of Medicine, St. Louis, MO, USA.
Jason J HanDivision of Cardiac Surgery, Department of Surgery, Cooper University Hospital, 24 S 6th Street, Camden, NJ, 08103, USA. han-jason@cooperhealth.edu.ORCID http://orcid.org/0000-0001-9288-3725

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionCaregivers of children with tetralogy of Fallot (TOF) frequently seek medical information online to better understand their child's diagnosis, treatment, and long-term care. Recently, generative artificial intelligence (AI) tools, including Google AI Overview and ChatGPT, have emerged as widely accessible sources of health information. However, the quality, readability, and accuracy of these platforms for caregiver education in congenital heart disease remain poorly characterized.

methodsForty frequently asked caregiver questions regarding TOF were identified using Google's "People Also Ask" feature. Each question was submitted to ChatGPT-5.5 and entered as a Google search query, generating 80 total responses. Google search output comprised 37 AI Overviews and three organic search excerpts. Responses were evaluated for word count, Flesch Reading Ease (FRE), and Flesch-Kincaid grade level (FKGL). Two independent reviewers assessed response accuracy using a 5-point Likert scale, while one reviewer evaluated educational quality using a modified Ensuring Quality Information for Patients (EQIP) instrument.

resultsCompared with Google search output (37 AI Overviews and three organic search excerpts), ChatGPT-5.5 generated significantly longer responses (327.9 ± 117.0 vs. 192.5 ± 85.9 words, p < 0.001), with a higher reading level (FKGL: 23.7 ± 8.5 vs. 15.4 ± 3.6, p < 0.001) and lower readability (FRE: 17.3 ± 14.9 vs. 27.4 ± 11.7, p < 0.001). ChatGPT-5.5 demonstrated higher reviewer-rated accuracy (4.95 ± 0.19 vs. 4.40 ± 0.44, p < 0.001) and higher modified EQIP scores (86.44% ± 7.01% vs. 59.62% ± 13.99%, p < 0.001).

conclusionsUnder the study's prompting conditions, ChatGPT-5.5 responses received higher reviewer-rated accuracy and modified EQIP scores than Google search output but were longer and had higher calculated reading-grade levels. These findings do not establish improved caregiver comprehension, which was not directly assessed.

Indexed as

Artificial intelligenceChatGPTCongenital heart diseaseHealth literacyLarge language modelsPatient educationReadabilityTetralogy of Fallot

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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.