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Diagnostic Performance and Error Patterns of a Large Language Model and Neural Network in Periodontitis

Agata Ossowska¹, Aida Kusiak¹, Albert Camlet¹

¹Department of Periodontology and Oral Mucosa Diseases, Medical University of Gdansk, 80-200 Gdansk, Poland.

Journal of Clinical Medicine

|June 26, 2026

Summary

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A neural network outperformed a large language model in diagnosing periodontitis staging and grading. The neural network achieved higher accuracy, while the large language model tended to underestimate disease severity.

Area of Science:

Periodontology
Artificial Intelligence
Medical Diagnostics

Background:

Periodontitis is a common chronic disease necessitating precise diagnosis for treatment.
Artificial intelligence (AI) offers potential for clinical decision support in periodontitis management.

Purpose of the Study:

To compare the diagnostic performance of a large language model (LLM) and a neural network (NN) for periodontitis classification.
To analyze classification error patterns between the LLM and NN using the current staging and grading system.

Main Methods:

Retrospective analysis of 110 patients with periodontal disease, including clinical and demographic data.
Evaluation of LLM and NN diagnostic performance using accuracy, confusion matrices, and Cohen's kappa coefficient.
Comparison of error patterns, including disease severity underestimation/overestimation by each AI model.

Keywords:

artificial intelligence diagnostic errors diagnostics disease classification large language models neural networks periodontitis

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Main Results:

The neural network achieved higher accuracy (85% for stage, 79% for grade) compared to the LLM (62% for stage, 63% for grade).
The LLM frequently underestimated disease severity, while the NN tended to overestimate progression.
Statistical analysis confirmed significant performance differences between the LLM and NN (p < 0.0001).

Conclusions:

A task-specific neural network demonstrated superior diagnostic performance over the evaluated large language model for periodontitis classification.
Differences in AI performance are linked to distinct training paradigms and intended applications.
Further research is essential to refine and validate AI tools for clinical periodontitis diagnosis.