Journal of cardiothoracic and vascular anesthesia
September 20, 2026 · via Europe PMC
ChatGPT Versus MediSearch: A Comparative Analysis of Artificial Intelligence Platforms for Patient Education in Cardiac Surgery and Cardiac Anesthesia.
Patel AP, Aggarwal I, Joseph S, Chen CH, Kakuturu J, Bailey CF
AI summary — for doctors, generated from the abstract only
Comparing two AI platforms for cardiac surgery patient education showed a trade-off: MediSearch was more reliable, while ChatGPT was more readable and accurate, and neither met sixth-grade readability.
What was studied
Cross-sectional evaluation of free versions of ChatGPT 4.0 and MediSearch answering 100 patient questions on cardiac surgery and anesthesia (80 fact, 14 policy, 6 value), assessed with modified DISCERN, Flesch-Kincaid and Coleman-Liau readability, and clinician-rated accuracy.
Key findings
- MediSearch had higher reliability (mean DISCERN 4.64 v 2.47).
- ChatGPT provided more readable and more accurate responses.
- Neither platform reached the sixth-grade readability level recommended for broad accessibility.
Clinical relevance
Clinicians should treat AI patient information tools with caution and review them for readability and accuracy before recommending them.
Who should care
Limitations
The abstract is cut off in the results, so readability scores and accuracy figures are missing. Only two platforms were tested at one time point, with no patients involved.
Neither AI tool is ready to serve as a standalone patient education resource for cardiac surgery.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
OBJECTIVES
To evaluate the reliability and readability of responses generated by two artificial intelligence (AI) platforms-ChatGPT and MediSearch-to common patient-focused questions about cardiac surgery and its anesthetic implications.
DESIGN
Prospective, comparative, cross-sectional evaluation.
SETTING
Single-institution academic study; no direct patient involvement. Questions were sourced from publicly available online data (Google "People Also Ask" feature).
PARTICIPANTS
One hundred unique patient-focused questions about cardiac surgery and anesthesia, categorized using the Rothwell classification system (80 fact, 14 policy, 6 value).
INTERVENTIONS
Each question was submitted individually to the free versions of ChatGPT (version 4.0) and MediSearch. Responses were evaluated using three validated metrics: reliability, assessed using the modified DISCERN tool; readability, assessed using the Flesch-Kincaid grade level (FK) and Coleman-Liau index (CL); and clinical accuracy, assessed by a board-certified cardiothoracic anesthesiologist and a board-certified cardiothoracic surgeon using a 3-point Likert scale.
MEASUREMENTS AND MAIN RESULTS
MediSearch demonstrated significantly higher reliability (mean DISCERN score, 4.64 v 2.47; p < 0.001). ChatGPT generated significantly more readable responses (mean FK score, 10.33 v 11.34 [p < 0.01]; mean CL score, 15.72 v 17.30 [p < 0.001]) and significantly higher clinical accuracy (mean, 0.82 v 0.71; p < 0.01). Subgroup analysis by Rothwell category confirmed MediSearch's reliability advantage across all question types, while ChatGPT's readability advantage was most pronounced for fact-based questions.
CONCLUSIONS
This study reveals a critical trade-off between evidence-based reliability and linguistic accessibility in AI-generated cardiac surgery patient education content. MediSearch offers superior reliability, while ChatGPT provides more readable and accurate responses. Neither platform achieves the sixth-grade readability level recommended for broad public accessibility. These findings underscore the need for AI platforms to balance clinical accuracy with health literacy standards before being endorsed as patient education tools in high-stakes settings.
DOI
10.1053/j.jvca.2026.09.032
PMID
42881058
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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