Artificial intelligence for image recognition in diagnosing oral and oropharyngeal cancer and leukoplakia
Abstract Visual diagnosis is one of the key features of squamous cell carcinoma of the oral cavity (OSCC) and oropharynx (OPSCC), both subsets of head and neck squamous cell carcinoma (HNSCC) with a heterogeneous clinical appearance. Advancements in artificial intelligence led to Image recognition b...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-025-85920-4 |
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| author | Benedikt Schmidl Tobias Hütten Steffi Pigorsch Fabian Stögbauer Cosima C. Hoch Timon Hussain Barbara Wollenberg Markus Wirth |
| author_facet | Benedikt Schmidl Tobias Hütten Steffi Pigorsch Fabian Stögbauer Cosima C. Hoch Timon Hussain Barbara Wollenberg Markus Wirth |
| author_sort | Benedikt Schmidl |
| collection | DOAJ |
| description | Abstract Visual diagnosis is one of the key features of squamous cell carcinoma of the oral cavity (OSCC) and oropharynx (OPSCC), both subsets of head and neck squamous cell carcinoma (HNSCC) with a heterogeneous clinical appearance. Advancements in artificial intelligence led to Image recognition being introduced recently into large language models (LLMs) such as ChatGPT 4.0. This exploratory study, for the first time, evaluated the application of image recognition by ChatGPT to diagnose squamous cell carcinoma and leukoplakia based on clinical images, with images without any lesion as a control group. A total of 45 clinical images were analyzed, comprising 15 cases each of SCC, leukoplakia, and non-lesion images. ChatGPT 4.0 was tasked with providing the most likely diagnosis based on these images in scenario one. In scenario two the image and the clinical history were provided, whereas in scenario three only the clinical history was given. The results and the accuracy of the LLM were rated by two independent reviewers and the overall performance was evaluated using the modified Artificial Intelligence Performance Index (AIPI. In this study, ChatGPT 4.0 demonstrated the ability to correctly identify leukoplakia cases using image recognition alone, while the ability to diagnose SCC was insufficient, but improved by including the clinical history in the prompt. Providing only the clinical history resulted in a misclassification of most leukoplakia and some SCC cases. Oral cavity lesions were more likely to be diagnosed correctly. In this exploratory study of 45 images of oral lesions, ChatGPT 4.0 demonstrated a convincing performance for detecting SCC only when the clinical history was added, whereas Leukoplakia was detected solely by image recognition. ChatGPT is therefore currently insufficient for reliable OPSCC and OSCC diagnosis, but further technological advancements may pave the way for the use in the clinical setting. |
| format | Article |
| id | doaj-art-cc56dd13e4024ea19970952eb894b58d |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-cc56dd13e4024ea19970952eb894b58d2025-02-02T12:24:20ZengNature PortfolioScientific Reports2045-23222025-01-0115111010.1038/s41598-025-85920-4Artificial intelligence for image recognition in diagnosing oral and oropharyngeal cancer and leukoplakiaBenedikt Schmidl0Tobias Hütten1Steffi Pigorsch2Fabian Stögbauer3Cosima C. Hoch4Timon Hussain5Barbara Wollenberg6Markus Wirth7Department of Otolaryngology Head and Neck Surgery, Technical University MunichDepartment of Otolaryngology Head and Neck Surgery, Technical University MunichDepartment of RadioOncology, Technical University MunichInstitute of Pathology, Technical University MunichDepartment of Otolaryngology Head and Neck Surgery, Technical University MunichDepartment of Otolaryngology Head and Neck Surgery, Technical University MunichDepartment of Otolaryngology Head and Neck Surgery, Technical University MunichDepartment of Otolaryngology Head and Neck Surgery, Technical University MunichAbstract Visual diagnosis is one of the key features of squamous cell carcinoma of the oral cavity (OSCC) and oropharynx (OPSCC), both subsets of head and neck squamous cell carcinoma (HNSCC) with a heterogeneous clinical appearance. Advancements in artificial intelligence led to Image recognition being introduced recently into large language models (LLMs) such as ChatGPT 4.0. This exploratory study, for the first time, evaluated the application of image recognition by ChatGPT to diagnose squamous cell carcinoma and leukoplakia based on clinical images, with images without any lesion as a control group. A total of 45 clinical images were analyzed, comprising 15 cases each of SCC, leukoplakia, and non-lesion images. ChatGPT 4.0 was tasked with providing the most likely diagnosis based on these images in scenario one. In scenario two the image and the clinical history were provided, whereas in scenario three only the clinical history was given. The results and the accuracy of the LLM were rated by two independent reviewers and the overall performance was evaluated using the modified Artificial Intelligence Performance Index (AIPI. In this study, ChatGPT 4.0 demonstrated the ability to correctly identify leukoplakia cases using image recognition alone, while the ability to diagnose SCC was insufficient, but improved by including the clinical history in the prompt. Providing only the clinical history resulted in a misclassification of most leukoplakia and some SCC cases. Oral cavity lesions were more likely to be diagnosed correctly. In this exploratory study of 45 images of oral lesions, ChatGPT 4.0 demonstrated a convincing performance for detecting SCC only when the clinical history was added, whereas Leukoplakia was detected solely by image recognition. ChatGPT is therefore currently insufficient for reliable OPSCC and OSCC diagnosis, but further technological advancements may pave the way for the use in the clinical setting.https://doi.org/10.1038/s41598-025-85920-4Image recognitionOSCCChatGPTOPSCCArtificial Intelligence |
| spellingShingle | Benedikt Schmidl Tobias Hütten Steffi Pigorsch Fabian Stögbauer Cosima C. Hoch Timon Hussain Barbara Wollenberg Markus Wirth Artificial intelligence for image recognition in diagnosing oral and oropharyngeal cancer and leukoplakia Scientific Reports Image recognition OSCC ChatGPT OPSCC Artificial Intelligence |
| title | Artificial intelligence for image recognition in diagnosing oral and oropharyngeal cancer and leukoplakia |
| title_full | Artificial intelligence for image recognition in diagnosing oral and oropharyngeal cancer and leukoplakia |
| title_fullStr | Artificial intelligence for image recognition in diagnosing oral and oropharyngeal cancer and leukoplakia |
| title_full_unstemmed | Artificial intelligence for image recognition in diagnosing oral and oropharyngeal cancer and leukoplakia |
| title_short | Artificial intelligence for image recognition in diagnosing oral and oropharyngeal cancer and leukoplakia |
| title_sort | artificial intelligence for image recognition in diagnosing oral and oropharyngeal cancer and leukoplakia |
| topic | Image recognition OSCC ChatGPT OPSCC Artificial Intelligence |
| url | https://doi.org/10.1038/s41598-025-85920-4 |
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