Evaluating the Utility of Generative AI Models for Synthetic Medical Image Generation: A Study on Panoramic Dental X-Rays

Elif Yılmaz; Abdullah Huzeyfe Köse; Rahime Yılmaz

Makale detayları

Evaluating the Utility of Generative AI Models for Synthetic Medical Image Generation: A Study on Panoramic Dental X-Rays

Yazarlar:

Elif Yılmaz | Abdullah Huzeyfe Köse | Rahime Yılmaz

Yayıncı:

Istanbul University

Ana Başlıklar:

Artificial Reality | Artificial Life and Complex Adaptive Systems | Artificial Intelligence (Other)

Dergi:

Journal of Data Analytics and Artificial Intelligence Applications Vol.1 Issue 2, pp.190-219

Belge Tipi:

Makale

Yayın Tarihi:

7/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001356125

pISSN:

3062-2697

Anahtar kelimeler:

Özet:

 Integrating artificial intelligence AI into dental practice can streamline processes that are otherwise manual and time-consuming. However, implementing AI in dentistry poses challenges, primarily due to the limited availability of clinical data, especially for rare cases, compounded by ethical and regulatory constraints. To address these challenges, synthetic data generation has increasingly been recognised as a promising solution. In this study, we aimed to evaluate the clinical usability of AI-generated panoramic dental X-rays produced by large-scale multimodal generative AIs, specifically GPT o4 mini high and Copilot, using textual prompts. These synthetic images were generated across multiple categories, enabling a comprehensive evaluation of the models capabilities. The primary objective was to determine whether AI generated images, produced from structured textual prompts and real sample descriptions, could be used for learning-based model training. For this purpose, an expert dentist evaluated the 61 generated synthetic images from a clinical perspective, focusing on their visual realism, anatomical accuracy, clinicaleduca tional utility, image clarity, and absence of artefacts. Both models generated images rated satisfactorily across all quality dimensions, with GPT o4 mini high achieving significantly superior anatomical fidelity and realism than Copilot. It was observed that neither system could consistently reproduce fine anatom ical details with pixel-level accuracy, a limitation that may prevent its use in applications requiring exact replication. Despite these limitations, the high quality and realism of the synthetic images highlight the potential of multimodal generative AIs. Thus, the studys findings suggest that synthetic panoramic images can reduce the reliance on scarce patient data, thereby accelerating the development of AI applications in dentistry.

 Integrating artificial intelligence AI into dental practice can streamline processes that are otherwise manual and time-consuming. However, implementing AI in dentistry poses challenges, primarily due to the limited availability of clinical data, especially for rare cases, compounded by ethical and regulatory constraints. To address these challenges, synthetic data generation has increasingly been recognised as a promising solution. In this study, we aimed to evaluate the clinical usability of AI-generated panoramic dental X-rays produced by large-scale multimodal generative AIs, specifically GPT o4 mini high and Copilot, using textual prompts. These synthetic images were generated across multiple categories, enabling a comprehensive evaluation of the models capabilities. The primary objective was to determine whether AI generated images, produced from structured textual prompts and real sample descriptions, could be used for learning-based model training. For this purpose, an expert dentist evaluated the 61 generated synthetic images from a clinical perspective, focusing on their visual realism, anatomical accuracy, clinicaleduca tional utility, image clarity, and absence of artefacts. Both models generated images rated satisfactorily across all quality dimensions, with GPT o4 mini high achieving significantly superior anatomical fidelity and realism than Copilot. It was observed that neither system could consistently reproduce fine anatom ical details with pixel-level accuracy, a limitation that may prevent its use in applications requiring exact replication. Despite these limitations, the high quality and realism of the synthetic images highlight the potential of multimodal generative AIs. Thus, the studys findings suggest that synthetic panoramic images can reduce the reliance on scarce patient data, thereby accelerating the development of AI applications in dentistry.

 Integrating artificial intelligence AI into dental practice can streamline processes that are otherwise manual and time-consuming. However, implementing AI in dentistry poses challenges, primarily due to the limited availability of clinical data, especially for rare cases, compounded by ethical and regulatory constraints. To address these challenges, synthetic data generation has increasingly been recognised as a promising solution. In this study, we aimed to evaluate the clinical usability of AI-generated panoramic dental X-rays produced by large-scale multimodal generative AIs, specifically GPT o4 mini high and Copilot, using textual prompts. These synthetic images were generated across multiple categories, enabling a comprehensive evaluation of the models capabilities. The primary objective was to determine whether AI generated images, produced from structured textual prompts and real sample descriptions, could be used for learning-based model training. For this purpose, an expert dentist evaluated the 61 generated synthetic images from a clinical perspective, focusing on their visual realism, anatomical accuracy, clinicaleduca tional utility, image clarity, and absence of artefacts. Both models generated images rated satisfactorily across all quality dimensions, with GPT o4 mini high achieving significantly superior anatomical fidelity and realism than Copilot. It was observed that neither system could consistently reproduce fine anatom ical details with pixel-level accuracy, a limitation that may prevent its use in applications requiring exact replication. Despite these limitations, the high quality and realism of the synthetic images highlight the potential of multimodal generative AIs. Thus, the studys findings suggest that synthetic panoramic images can reduce the reliance on scarce patient data, thereby accelerating the development of AI applications in dentistry.

 Integrating artificial intelligence AI into dental practice can streamline processes that are otherwise manual and time-consuming. However, implementing AI in dentistry poses challenges, primarily due to the limited availability of clinical data, especially for rare cases, compounded by ethical and regulatory constraints. To address these challenges, synthetic data generation has increasingly been recognised as a promising solution. In this study, we aimed to evaluate the clinical usability of AI-generated panoramic dental X-rays produced by large-scale multimodal generative AIs, specifically GPT o4 mini high and Copilot, using textual prompts. These synthetic images were generated across multiple categories, enabling a comprehensive evaluation of the models capabilities. The primary objective was to determine whether AI generated images, produced from structured textual prompts and real sample descriptions, could be used for learning-based model training. For this purpose, an expert dentist evaluated the 61 generated synthetic images from a clinical perspective, focusing on their visual realism, anatomical accuracy, clinicaleduca tional utility, image clarity, and absence of artefacts. Both models generated images rated satisfactorily across all quality dimensions, with GPT o4 mini high achieving significantly superior anatomical fidelity and realism than Copilot. It was observed that neither system could consistently reproduce fine anatom ical details with pixel-level accuracy, a limitation that may prevent its use in applications requiring exact replication. Despite these limitations, the high quality and realism of the synthetic images highlight the potential of multimodal generative AIs. Thus, the studys findings suggest that synthetic panoramic images can reduce the reliance on scarce patient data, thereby accelerating the development of AI applications in dentistry.

Makale detayları

Evaluating the Utility of Generative AI Models for Synthetic Medical Image Generation: A Study on Panoramic Dental X-Rays

Yazarlar:

Elif Yılmaz | Abdullah Huzeyfe Köse | Rahime Yılmaz

Yayıncı:

Istanbul University

Ana Başlıklar:

Artificial Reality | Artificial Life and Complex Adaptive Systems | Artificial Intelligence (Other)

Dergi:

Journal of Data Analytics and Artificial Intelligence Applications Vol.1 Issue 2, pp.190-219

Belge Tipi:

Makale

Yayın Tarihi:

7/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001356125

pISSN:

3062-2697

Synthetic Data Generation

Dental Radiography

Multimodal Generative AI

Clinical Usability

Data Scarcity in Dentistry

Sayı

Makale detayları

Evaluating the Utility of Generative AI Models for Synthetic Medical Image Generation: A Study on Panoramic Dental X-Rays

Yazarlar: Elif Yılmaz | Abdullah Huzeyfe Köse | Rahime Yılmaz

Yayıncı: Istanbul University

Ana Başlıklar: Artificial Reality | Artificial Life and Complex Adaptive Systems | Artificial Intelligence (Other)

Dergi: Journal of Data Analytics and Artificial Intelligence Applications Vol.1 Issue 2, pp.190-219

Belge Tipi: Makale

Yayın Tarihi: 7/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001356125

pISSN: 3062-2697

Synthetic Data Generation

Dental Radiography

Multimodal Generative AI

Clinical Usability

Data Scarcity in Dentistry

Sayı

Yazarlar:

Elif Yılmaz | Abdullah Huzeyfe Köse | Rahime Yılmaz

Yayıncı:

Istanbul University

Ana Başlıklar:

Artificial Reality | Artificial Life and Complex Adaptive Systems | Artificial Intelligence (Other)

Dergi:

Journal of Data Analytics and Artificial Intelligence Applications Vol.1 Issue 2, pp.190-219

Belge Tipi:

Makale

Yayın Tarihi:

7/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001356125

pISSN:

3062-2697