Artificial Intelligence Diagnosing of Oral Lichen Planus: A Comparative Study DOI Creative Commons

Shandong Yu,

Wansu Sun,

DaWei Mi

et al.

Bioengineering, Journal Year: 2024, Volume and Issue: 11(11), P. 1159 - 1159

Published: Nov. 18, 2024

Early diagnosis of oral lichen planus (OLP) is challenging, which traditionally dependent on clinical experience and subjective interpretation. Artificial intelligence (AI) technology has been widely applied in objective rapid diagnoses. In this study, we aim to investigate the potential AI OLP evaluate its effectiveness improving diagnostic accuracy accelerating decision making. A total 128 confirmed patients were included, lesion images from various anatomical sites collected. The was performed using platforms, including ChatGPT-4O, ChatGPT (Diagram-Date extension), Claude Opus, for directly identification pre-training identification. After feature training, platforms significantly improved, with overall recognition rates Opus increasing 59%, 68%, 15% 77%, 80%, 50%, respectively. Additionally, buccal mucosa reached 94%, 93%, 56%, However, less effectively when recognizing lesions common complex cases; instance, gums only 60%, 20%, demonstrating significant limitations. study highlights strengths limitations different technologies provides a reference future applications medicine.

Language: Английский

Artificial Intelligence Diagnosing of Oral Lichen Planus: A Comparative Study DOI Creative Commons

Shandong Yu,

Wansu Sun,

DaWei Mi

et al.

Bioengineering, Journal Year: 2024, Volume and Issue: 11(11), P. 1159 - 1159

Published: Nov. 18, 2024

Early diagnosis of oral lichen planus (OLP) is challenging, which traditionally dependent on clinical experience and subjective interpretation. Artificial intelligence (AI) technology has been widely applied in objective rapid diagnoses. In this study, we aim to investigate the potential AI OLP evaluate its effectiveness improving diagnostic accuracy accelerating decision making. A total 128 confirmed patients were included, lesion images from various anatomical sites collected. The was performed using platforms, including ChatGPT-4O, ChatGPT (Diagram-Date extension), Claude Opus, for directly identification pre-training identification. After feature training, platforms significantly improved, with overall recognition rates Opus increasing 59%, 68%, 15% 77%, 80%, 50%, respectively. Additionally, buccal mucosa reached 94%, 93%, 56%, However, less effectively when recognizing lesions common complex cases; instance, gums only 60%, 20%, demonstrating significant limitations. study highlights strengths limitations different technologies provides a reference future applications medicine.

Language: Английский

Citations

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