Accuracy and readability of artificial intelligence models in providing orthodontic retention related information: A cross-sectional study
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By
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Afnan Ben Gassem
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Nebras Althagafi
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May 21, 2026
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0 min
Clinical Report: Evaluating the Precision and Comprehensibility of AI Models
Overview
This study evaluates the reliability of orthodontic retention information generated by AI models, specifically ChatGPT 3.5, ChatGPT 4, Gemini, and Copilot. The findings highlight significant variability in accuracy, relevance, and readability of the information provided by these models.
Background
The use of AI in healthcare, particularly in dentistry, is growing, with patients increasingly seeking information from AI-based platforms. Orthodontic retention is critical for maintaining treatment outcomes, yet the reliability of AI-generated information in this area has not been systematically assessed. Understanding the performance of AI tools in delivering accurate retention information is essential for patient education and treatment success.
Data Highlights
No numerical data or trial data was provided in the source material.
Key Findings
- AI models can provide generally accurate information, but reliability varies across models.
- Readability of AI-generated content may be too complex for patients to understand.
- Incorrect information regarding retainer use can negatively impact patient satisfaction and treatment outcomes.
- The study emphasizes the need for careful evaluation of AI-generated orthodontic retention information.
- AI tools like ChatGPT have shown variability in citation behavior and depth of information.
Clinical Implications
Healthcare professionals should be cautious when utilizing AI-generated information for patient education, particularly in specialized fields like orthodontics. Ensuring the accuracy and comprehensibility of information is vital for effective patient communication and treatment adherence.
Conclusion
The study underscores the importance of evaluating AI tools in delivering reliable orthodontic retention information, which is crucial for patient care and treatment outcomes.
Related Resources & Content
- Retention in orthodontics: an evidence-based overview, ScienceDirect, 2025 -- Retention in orthodontics: an evidence-based overview
- Development of a clinical practice guideline for orthodontic retention, PMC, 2019 -- Development of a clinical practice guideline for orthodontic retention
- Patient trust in artificial intelligence for orthodontic advice: a systematic review, ScienceDirect, 2025 -- Patient trust in artificial intelligence for orthodontic advice
- Precision of Jaw Computer-Aided Design Models Generated from Ultra-Low MDCT Doses Utilizing ASIR and MBIR Techniques
- Perspectives of Future Orthopaedic Surgeons on Artificial Intelligence: A Cross-National Survey Analysis
- compendium — Comparison of Different Centric Relation Recording Techniques Using a Digital Occlusal Analyzer: An In Vivo Study
- compendium — Skeletal, Dentoalveolar, Dental, and Soft-Tissue Effects Following MARPE Treatment: A Review — Table 3
- British Orthodontic Society Clinical Guidelines
- Outcomes assessed in clinical trials concerning orthodontic retention: A scoping review, ScienceDirect
- Retention in orthodontics: an evidence-based overview - ScienceDirect
- Development of a clinical practice guideline for orthodontic retention - PMC
- A comparison of survival rates using 3 methods of mandibular fixed retainer fabrication: A randomized clinical trial - PubMed
- Survival rates of mandibular fixed retainers: comparison of a tube-type retainer and conventional multistrand retainers : A prospective randomized clinical trial - PubMed
- Assessing the effectiveness of smart retainers for orthodontic retention: A systematic review and meta-analysis - ScienceDirect
- AAO Publishes Position Paper to Guide Clinical Use of AI - AAO
- Information from digital and human sources: A comparison of chatbot and clinician responses to orthodontic questions - ScienceDirect
- Patient trust in artificial intelligence for orthodontic advice: A systematic review - ScienceDirect
This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.
