Evaluating the Response of AI-Based Large Language Models to Common Patient Concerns About Endodontic Root Canal Treatment: A Comparative Performance Analysis

<b>Objectives:</b> The aim of this study was to compare the responses of large language models (LLMs)-DeepSeek V3, GPT 5, and Gemini 2.5 Flash-to patients' frequently asked questions (FAQs) regarding root canal treatment in terms of accuracy and comprehensiveness, and to assess the potential roles of these models in patient education and health literacy. <b>Methods:</b> A total of 37 open-ended FAQs, compiled from American Association of Endodontists (AAE) patient education materials and online resources, were presented to three LLMs. Responses were evaluated by expert clinicians on a 5-point Likert scale for accuracy and comprehensiveness. Inter-rater and test-retest reliability were assessed using intraclass correlation coefficients (ICCs). Differences among models were analyzed with the Kruskal-Wallis H test, followed by pairwise Mann-Whitney U tests with effect sizes (Cliff's delta, δ). A <i>p</i>-value < 0.05 was considered statistically significant. <b>Results:</b> Inter-rater agreement was excellent, with ICCs of 0.92 for accuracy and 0.91 for comprehensiveness. Test-retest reliability also demonstrated high consistency (ICCs of 0.90 for accuracy and 0.89 for comprehensiveness). DeepSeek V3 achieved the highest scores, with a mean accuracy of 4.81 ± 0.39 and a mean comprehensiveness of 4.78 ± 0.41, demonstrating statistically superior performance compared to GPT 5 (accuracy 4.0 ± 0.0; comprehensiveness 4.05 ± 0.4; <i>p</i> < 0.05, δ = 0.81 for accuracy, δ = 0.69 for comprehensiveness) and Gemini 2.5 Flash (accuracy 3.83 ± 0.68; comprehensiveness 3.81 ± 0.7; <i>p</i> < 0.05, δ = 0.71 for accuracy, δ = 0.70 for comprehensiveness). No significant difference was observed between GPT 5 and Gemini 2.5 Flash for either accuracy (<i>p</i> = 0.109, δ = 0.16) or comprehensiveness (<i>p</i> = 0.058, δ = 0.21). <b>Conclusions:</b> LLMs, such as DeepSeek V3, which can provide satisfactory responses to FAQs may serve as valuable supportive tools in patient education and health literacy; however, expert clinician oversight remains essential in clinical decision-making and treatment planning. When used appropriately, LLMs can enhance patient awareness and support satisfaction throughout the root canal treatment.

Authors

Institutions

Publication Details

Journal
Journal of Clinical Medicine
Published
2025-10-22
DOI
https://doi.org/10.3390/jcm14217482
Citations
2
Primary Topic
Dental Radiography and Imaging
Type
article
Field-Weighted Citation Impact
3.58
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evaluating the Response of AI-Based Large Language Models to Common Patient Concerns About Endodontic Root Canal Treatment: A Comparative Performance Analysis

Orhan Çiçek, Busra Demir Cicek
2 citations
Journal of Clinical Medicine
Dental Radiography and Imaging
3.58
article

Evaluating the Response of AI-Based Large Language Models to Common Patient Concerns About Endodontic Root Canal Treatment: A Comparative Performance Analysis

Orhan Çiçek, Busra Demir Cicek
article en
2 citations

Abstract

<b>Objectives:</b> The aim of this study was to compare the responses of large language models (LLMs)-DeepSeek V3, GPT 5, and Gemini 2.5 Flash-to patients' frequently asked questions (FAQs) regarding root canal treatment in terms of accuracy and comprehensiveness, and to assess the potential roles of these models in patient education and health literacy. <b>Methods:</b> A total of 37 open-ended FAQs, compiled from American Association of Endodontists (AAE) patient education materials and online resources, were presented to three LLMs. Responses were evaluated by expert clinicians on a 5-point Likert scale for accuracy and comprehensiveness. Inter-rater and test-retest reliability were assessed using intraclass correlation coefficients (ICCs). Differences among models were analyzed with the Kruskal-Wallis H test, followed by pairwise Mann-Whitney U tests with effect sizes (Cliff's delta, δ). A <i>p</i>-value < 0.05 was considered statistically significant. <b>Results:</b> Inter-rater agreement was excellent, with ICCs of 0.92 for accuracy and 0.91 for comprehensiveness. Test-retest reliability also demonstrated high consistency (ICCs of 0.90 for accuracy and 0.89 for comprehensiveness). DeepSeek V3 achieved the highest scores, with a mean accuracy of 4.81 ± 0.39 and a mean comprehensiveness of 4.78 ± 0.41, demonstrating statistically superior performance compared to GPT 5 (accuracy 4.0 ± 0.0; comprehensiveness 4.05 ± 0.4; <i>p</i> < 0.05, δ = 0.81 for accuracy, δ = 0.69 for comprehensiveness) and Gemini 2.5 Flash (accuracy 3.83 ± 0.68; comprehensiveness 3.81 ± 0.7; <i>p</i> < 0.05, δ = 0.71 for accuracy, δ = 0.70 for comprehensiveness). No significant difference was observed between GPT 5 and Gemini 2.5 Flash for either accuracy (<i>p</i> = 0.109, δ = 0.16) or comprehensiveness (<i>p</i> = 0.058, δ = 0.21). <b>Conclusions:</b> LLMs, such as DeepSeek V3, which can provide satisfactory responses to FAQs may serve as valuable supportive tools in patient education and health literacy; however, expert clinician oversight remains essential in clinical decision-making and treatment planning. When used appropriately, LLMs can enhance patient awareness and support satisfaction throughout the root canal treatment.

Journal of Clinical MedicineVol. 14(21)
Zonguldak Bülent Ecevit University (TR)
Openalex Percentile: Top 7%
Dental Radiography and Imaging
3.58
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.