ArticleScientific reports2025
Artificial intelligence for image recognition in diagnosing oral and oropharyngeal cancer and leukoplakia.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Clinical decision support using large language models in otolaryngology: a systematic review.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Pooled it
- Commercial large language models for oral cavity cancer staging using descriptive pre-treatment MRI reports: ready for standalone use in clinical practice?Insights into imaging · 2026Article
- Diagnostic performance of a multimodal large language model in assessing the index of complexity, outcome, and need: comparison with orthodontist evaluation.BMC oral health · 2026Article
- Do Multimodal Vision-Language Models Enhance the Medical Diagnostic Process? A Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Diagnostic Performance of Contemporary Large Language Models on Free-Text Histopathologic Descriptions in Oral and Maxillofacial Pathology.Head and neck pathology · 2026Article
- Artificial intelligence for diagnosis and triage in oral cancer: a clinician‑centered narrative review.International journal of clinical oncology · 2026Review
- Bi-linguistic performance of large language models in multimodal analysis for differentiating jawbone-destroying malignancy from osteomyelitis.BMC oral health · 2026Article
- Use of Artificial Intelligence for Diagnosing Oral Mucosa Conditions: A Review.Diagnostics (Basel, Switzerland) · 2026Review
- Telemedicine in Oral and Maxillofacial Surgery: A Narrative Review of Clinical Applications, Outcomes and Future Directions.Journal of clinical medicine · 2026Review
- Prospective applications of artificial intelligence for the diagnosis of oral leukoplakia: a scoping review.Frontiers in oral health · 2026Review
- Advances in oral disease models: a mini-review of developments from 2015 to 2025.Frontiers in dental medicine · 2026Review
- Multimodal large language models for oral lesion diagnosis: a systematic review of diagnostic performance and clinical utility.Frontiers in oral health · 2026Review
- ChatGPT in Oral Pathology: Bright Promise or Diagnostic Mirage.Medicina (Kaunas, Lithuania) · 2025Article
- DeepSeek: Another step forward in the diagnosis of oral lesions.Journal of dental sciences · 2025Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.