ArticleClinical oral investigations2025
Exploring GPT-4o's multimodal reasoning capabilities with panoramic radiograph: the role of prompt engineering.
Article in Clinical oral investigations, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Diagnostic accuracy of large language models in the classification of superior labial frenulum attachments.Odontology · 2026Article
- RadCoT: a Radiological Chain-of-Thought framework for enhanced error detection in radiology reports.European radiology experimental · 2026Article
- Diagnostic and localization performance of multimodal large language models in the interpretation of dental periapical radiographs.BMC oral health · 2026Article
- How Reliably Do Large Language Models Reproduce Vital Pulp Therapy Guidelines? A Mixed-Effects Evaluation of Guideline-Concordance and Error Directionality.Healthcare (Basel, Switzerland) · 2026Article
- Comparative Performance of State-of-the-Art LLMs on the KDLE: A 2025 Benchmark Study.International dental journal · 2026Article
- Assessing GPT-4o in cataract surgery decision-making: appropriateness, consistency, and clinical implications.Frontiers in artificial intelligence · 2026Article
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7 authors.
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Abstract
objectivesThe aim of this study was to evaluate GPT-4o's multimodal reasoning ability to review panoramic radiograph (PR) and verify its radiologic findings, while exploring the role of prompt engineering in enhancing its performance.
methodsThe study included 230 PRs from West China Hospital of Stomatology in 2024, which were interpreted to generate the PR findings. A total of 300 instances of interpretation errors, were manually inserted into the PR findings. The ablation study was conducted to assess whether GPT-4o can perform reasoning on PR under a zero-shot prompt. Prompt engineering was employed to enhance the reasoning capabilities of GPT-4o in identifying interpretation errors with PRs. The prompt strategies included chain-of-thought, self-consistency, in-context learning, multimodal in-context learning, and their systematic integration into a meta-prompt. Recall, accuracy, and F1 score were employed to evaluate the outputs. Subsequently, the localization capability of GPT-4o and its influence on reasoning capability were evaluated.
resultsIn the ablation study, GPT-4o's recall increased significantly from 2.67 to 43.33% upon acquiring PRs (P < 0.001). GPT-4o with the meta prompt demonstrated improvements in recall (43.33% vs. 52.67%, P = 0.022), accuracy (39.95% vs. 68.75%, P < 0.001), and F1 score (0.42 vs. 0.60, P < 0.001) compared to the zero-shot prompt and other prompt strategies. The localization accuracy of GPT-4o was 45.67% (137 out of 300, 95% CI: 40.00 to 51.34). A significant correlation was observed between its localization accuracy and reasoning capability under the meta prompt (φ coefficient = 0.33, p < 0.001). The model's recall increased by 5.49% (P = 0.031) by providing accurate localization cues within the meta prompt.
conclusionsGPT-4o demonstrated a certain degree of multimodal capability for PR, with performance enhancement through prompt engineering. Nevertheless, its performance remains inadequate for clinical requirements. Future efforts will be necessary to identify additional factors influencing the model's reasoning capability or to develop more advanced models. CLINICAL RELEVANCE: Evaluating GPT-4o's capability to interpret and reason through PRs and exploring potential methods to enhance its performance before clinical application in assisting radiological assessments.
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