UlcerGPT: A Multimodal Approach Leveraging Large Language and Vision Models for Diabetic Foot Ulcer Image Transcription
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913528829444096 |
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| author | Basiri, Reza Abedi, Ali Nguyen, Chau Popovic, Milos R. Khan, Shehroz S. |
| author_facet | Basiri, Reza Abedi, Ali Nguyen, Chau Popovic, Milos R. Khan, Shehroz S. |
| contents | Diabetic foot ulcers (DFUs) are a leading cause of hospitalizations and lower limb amputations, placing a substantial burden on patients and healthcare systems. Early detection and accurate classification of DFUs are critical for preventing serious complications, yet many patients experience delays in receiving care due to limited access to specialized services. Telehealth has emerged as a promising solution, improving access to care and reducing the need for in-person visits. The integration of artificial intelligence and pattern recognition into telemedicine has further enhanced DFU management by enabling automatic detection, classification, and monitoring from images. Despite advancements in artificial intelligence-driven approaches for DFU image analysis, the application of large language models for DFU image transcription has not yet been explored. To address this gap, we introduce UlcerGPT, a novel multimodal approach leveraging large language and vision models for DFU image transcription. This framework combines advanced vision and language models, such as Large Language and Vision Assistant and Chat Generative Pre-trained Transformer, to transcribe DFU images by jointly detecting, classifying, and localizing regions of interest. Through detailed experiments on a public dataset, evaluated by expert clinicians, UlcerGPT demonstrates promising results in the accuracy and efficiency of DFU transcription, offering potential support for clinicians in delivering timely care via telemedicine. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_01989 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | UlcerGPT: A Multimodal Approach Leveraging Large Language and Vision Models for Diabetic Foot Ulcer Image Transcription Basiri, Reza Abedi, Ali Nguyen, Chau Popovic, Milos R. Khan, Shehroz S. Computer Vision and Pattern Recognition Artificial Intelligence Diabetic foot ulcers (DFUs) are a leading cause of hospitalizations and lower limb amputations, placing a substantial burden on patients and healthcare systems. Early detection and accurate classification of DFUs are critical for preventing serious complications, yet many patients experience delays in receiving care due to limited access to specialized services. Telehealth has emerged as a promising solution, improving access to care and reducing the need for in-person visits. The integration of artificial intelligence and pattern recognition into telemedicine has further enhanced DFU management by enabling automatic detection, classification, and monitoring from images. Despite advancements in artificial intelligence-driven approaches for DFU image analysis, the application of large language models for DFU image transcription has not yet been explored. To address this gap, we introduce UlcerGPT, a novel multimodal approach leveraging large language and vision models for DFU image transcription. This framework combines advanced vision and language models, such as Large Language and Vision Assistant and Chat Generative Pre-trained Transformer, to transcribe DFU images by jointly detecting, classifying, and localizing regions of interest. Through detailed experiments on a public dataset, evaluated by expert clinicians, UlcerGPT demonstrates promising results in the accuracy and efficiency of DFU transcription, offering potential support for clinicians in delivering timely care via telemedicine. |
| title | UlcerGPT: A Multimodal Approach Leveraging Large Language and Vision Models for Diabetic Foot Ulcer Image Transcription |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2410.01989 |