{"title":"利用人工智能为口腔肿瘤学建立预测模型:机遇、挑战与临床展望","authors":"Vishnu Priya Veeraraghavan , Shikhar Daniel , Arun Kumar Dasari , Kaladhar Reddy Aileni , Chaitra patil , Santosh R. Patil","doi":"10.1016/j.oor.2024.100591","DOIUrl":null,"url":null,"abstract":"<div><p>Artificial intelligence (AI) has emerged as a promising tool in oral oncology, particularly in the field of prediction. This review provides a comprehensive outlook on the role of AI in predicting oral cancer, covering key aspects such as data collection and preprocessing, machine learning techniques, performance evaluation and validation, challenges, future prospects, and implications for clinical practice. Various AI algorithms, including supervised learning, unsupervised learning, and deep learning approaches, have been discussed in the context of oral cancer prediction. Additionally, challenges such as interpretability, data accessibility, regulatory compliance, and legal implications are addressed along with future research directions and the potential impact of AI on oral oncology care.</p></div>","PeriodicalId":94378,"journal":{"name":"Oral Oncology Reports","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772906024004370/pdfft?md5=fcb418c11ec3fe96695431c518f5d01d&pid=1-s2.0-S2772906024004370-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Harnessing artificial intelligence for predictive modelling in oral oncology: Opportunities, challenges, and clinical Perspectives\",\"authors\":\"Vishnu Priya Veeraraghavan , Shikhar Daniel , Arun Kumar Dasari , Kaladhar Reddy Aileni , Chaitra patil , Santosh R. Patil\",\"doi\":\"10.1016/j.oor.2024.100591\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Artificial intelligence (AI) has emerged as a promising tool in oral oncology, particularly in the field of prediction. This review provides a comprehensive outlook on the role of AI in predicting oral cancer, covering key aspects such as data collection and preprocessing, machine learning techniques, performance evaluation and validation, challenges, future prospects, and implications for clinical practice. Various AI algorithms, including supervised learning, unsupervised learning, and deep learning approaches, have been discussed in the context of oral cancer prediction. Additionally, challenges such as interpretability, data accessibility, regulatory compliance, and legal implications are addressed along with future research directions and the potential impact of AI on oral oncology care.</p></div>\",\"PeriodicalId\":94378,\"journal\":{\"name\":\"Oral Oncology Reports\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2772906024004370/pdfft?md5=fcb418c11ec3fe96695431c518f5d01d&pid=1-s2.0-S2772906024004370-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Oral Oncology Reports\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2772906024004370\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Oral Oncology Reports","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772906024004370","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Harnessing artificial intelligence for predictive modelling in oral oncology: Opportunities, challenges, and clinical Perspectives
Artificial intelligence (AI) has emerged as a promising tool in oral oncology, particularly in the field of prediction. This review provides a comprehensive outlook on the role of AI in predicting oral cancer, covering key aspects such as data collection and preprocessing, machine learning techniques, performance evaluation and validation, challenges, future prospects, and implications for clinical practice. Various AI algorithms, including supervised learning, unsupervised learning, and deep learning approaches, have been discussed in the context of oral cancer prediction. Additionally, challenges such as interpretability, data accessibility, regulatory compliance, and legal implications are addressed along with future research directions and the potential impact of AI on oral oncology care.