Xuze Guo, Yaozu He, Qi Han, Jialin Xie, Yi Jia, You Li, Fanglong Wu
{"title":"Application of artificial intelligence in oral potentially malignant disorders: current opinions and future barriers.","authors":"Xuze Guo, Yaozu He, Qi Han, Jialin Xie, Yi Jia, You Li, Fanglong Wu","doi":"10.1007/s12094-025-04043-4","DOIUrl":null,"url":null,"abstract":"<p><p>Oral potentially malignant disorders (OPMDs) refer to oral mucosal disorders with an increased risk of malignancy, primarily oral squamous cell carcinoma (OSCC), especially in South and Southeast Asia. Since not all patients with OPMDs develop oral cancer, accurate early detection and diagnosis of malignant transformation are critically important for clinicians to determine the optimal therapeutic approach. Therefore, distinguishing OPMDs from early-stage OSCC is an increasing challenge in the clinic. Artificial intelligence (AI) technology has recently been shown to quickly identify high-risk conditions/lesions for screening oral cancer early. Moreover, the AI algorithm can also be used to determine the prognosis of OPMDs. In this review, we systematically summarize the medical records, oral images, pathological examinations, biomarkers, omics data and other aspects of the main outcomes of AI applied to address OPMDs-related issues. Furthermore, we discuss automated diagnostic systems and risk prediction tools for malignant transformation with pleasant outcomes and the potential to ultimately assist clinicians. Finally, we introduce the current challenges and barriers to AI in OPMDs on the premise that more advanced AI models and larger datasets will lead to the use of AI models in OPMDs.</p>","PeriodicalId":50685,"journal":{"name":"Clinical & Translational Oncology","volume":" ","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical & Translational Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12094-025-04043-4","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Oral potentially malignant disorders (OPMDs) refer to oral mucosal disorders with an increased risk of malignancy, primarily oral squamous cell carcinoma (OSCC), especially in South and Southeast Asia. Since not all patients with OPMDs develop oral cancer, accurate early detection and diagnosis of malignant transformation are critically important for clinicians to determine the optimal therapeutic approach. Therefore, distinguishing OPMDs from early-stage OSCC is an increasing challenge in the clinic. Artificial intelligence (AI) technology has recently been shown to quickly identify high-risk conditions/lesions for screening oral cancer early. Moreover, the AI algorithm can also be used to determine the prognosis of OPMDs. In this review, we systematically summarize the medical records, oral images, pathological examinations, biomarkers, omics data and other aspects of the main outcomes of AI applied to address OPMDs-related issues. Furthermore, we discuss automated diagnostic systems and risk prediction tools for malignant transformation with pleasant outcomes and the potential to ultimately assist clinicians. Finally, we introduce the current challenges and barriers to AI in OPMDs on the premise that more advanced AI models and larger datasets will lead to the use of AI models in OPMDs.
期刊介绍:
Clinical and Translational Oncology is an international journal devoted to fostering interaction between experimental and clinical oncology. It covers all aspects of research on cancer, from the more basic discoveries dealing with both cell and molecular biology of tumour cells, to the most advanced clinical assays of conventional and new drugs. In addition, the journal has a strong commitment to facilitating the transfer of knowledge from the basic laboratory to the clinical practice, with the publication of educational series devoted to closing the gap between molecular and clinical oncologists. Molecular biology of tumours, identification of new targets for cancer therapy, and new technologies for research and treatment of cancer are the major themes covered by the educational series. Full research articles on a broad spectrum of subjects, including the molecular and cellular bases of disease, aetiology, pathophysiology, pathology, epidemiology, clinical features, and the diagnosis, prognosis and treatment of cancer, will be considered for publication.