Evidence map›Paper›PMID 42338855›Full record

ArticleCureus2026

Assessing Artificial Intelligence (AI) in Patient Education: Evaluating Accuracy and Readability of Responses on Surgical Procedures for Patellar Tendon Rupture.

Monica Guirgus, Peter A Giammanco, Christopher Collins, Sunny Trivedi, Richard C Rice, Rusheel Nayak, Tate Cowley, Samuel Baird, Matthew Gulbrandsen, Joseph Elsissy

Abstract read
In one paragraph

Article in Cureus, 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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0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Monica GuirgusOrthopedics, California University of Science and Medicine, Colton, USA.
Peter A GiammancoOrthopedic Surgery, Arrowhead Regional Medical Center, Colton, USA.
Christopher CollinsOrthopedic Surgery, California University of Science and Medicine, Colton, USA.
Sunny TrivediOrthopedic Surgery, Loma Linda University Medical Center, Loma Linda, USA.
Richard C RiceOrthopedic Surgery, Loma Linda University Medical Center, Loma Linda, USA.
Rusheel NayakOrthopedic Surgery, Loma Linda University Medical Center, Loma Linda, USA.
Tate CowleyOrthopedic Surgery, Loma Linda University Medical Center, Loma Linda, USA.
Samuel BairdOrthopedic Surgery, Loma Linda University Medical Center, Loma Linda, USA.
Matthew GulbrandsenOrthopedic Surgery, Loma Linda University Medical Center, Loma Linda, USA.
Joseph ElsissyOrthopedic Surgery, Arrowhead Regional Medical Center, Colton, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction Patients' utilization of the internet as a resource for obtaining medical information continues to expand, with increased prevalence and access to educational materials. One method of obtaining medical information online is artificial intelligence (AI)-generated patient education materials (PEMs). As such, the medical community has a fundamental obligation to assess the accuracy, quality, and readability of AI-generated PEMs as patient resources - a critical step in promoting health literacy, combating misinformation, and, ultimately, empowering patients. Given that the perceived severity of patellar tendon ruptures (PTR) can vary, providing clear information is important to support informed decision-making. This study aimed to evaluate and compare the readability and quality of AI-generated responses to patient questions about patellar tendon repair, using four different AI chatbots: ChatGPT 3.5, ChatGPT 4, Gemini 1.0, and Perplexity. Methods There were no significant differences in readability among the four different chatbots, and they all provided responses that were better than the average American reading level. The mean DISCERN scores were as follows: Perplexity (64.2±9.2), ChatGPT 3.5 (49±7.97), Gemini 1.0 (59.2±7.43), and ChatGPT 4 (52±6.28). Even though Perplexity demonstrated the highest mean DISCERN scores among the evaluated AI models, no statistically significant differences in readability were observed among the four chatbots, although results approached significance (p = 0.075). Question 15 of the DISCERN criteria, regarding shared decision-making, was consistently rated at a high level across each AI tool, with an average rating of 4.2 out of 5.  Results There were no significant differences in readability among the four different chatbots and they all provided responses that averaged above the average American reading level. The mean DISCERN scores were as follows: Perplexity (64.2±9.2), ChatGPT 3.5 (49±7.97), Gemini 1.0 (59.2±7.43), and ChatGPT 4 (52±6.28). Perplexity's score was statistically significant when compared to ChatGPT3.5, indicating that the responses of Perplexity were more accurate and reliable than ChatGPT3.5. Question 15 of the DISCERN criteria, regarding shared decision-making, was consistently rated at a high level across each AI tool, with an average rating of 4.2 out of 5.  Conclusion This study found that readability remains consistent across various AI tools, while the quality of the information may vary. Perplexity outperformed ChatGPT 3.5 in providing accurate information on patellar tendon ruptures. AI tools demonstrated variability in informational quality scores, although these differences were not statistically significant, highlighting the importance of carefully evaluating AI-generated content before using it as a patient education resource.

Indexed as

artificial intelligencechatbotchatgptpatellar tendon rupturepatient education

Identifiers

PMID42338855
PMCPMC13286298

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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.