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 technique...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-09-01
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| Series: | Oral Oncology Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772906024004370 |
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| author | Vishnu Priya Veeraraghavan Shikhar Daniel Arun Kumar Dasari Kaladhar Reddy Aileni Chaitra patil Santosh R. Patil |
| author_facet | Vishnu Priya Veeraraghavan Shikhar Daniel Arun Kumar Dasari Kaladhar Reddy Aileni Chaitra patil Santosh R. Patil |
| author_sort | Vishnu Priya Veeraraghavan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-09bda21d7fb44011bb830e1f48921940 |
| institution | Kabale University |
| issn | 2772-9060 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Oral Oncology Reports |
| spelling | doaj-art-09bda21d7fb44011bb830e1f489219402025-01-09T06:16:51ZengElsevierOral Oncology Reports2772-90602024-09-0111100591Harnessing artificial intelligence for predictive modelling in oral oncology: Opportunities, challenges, and clinical PerspectivesVishnu Priya Veeraraghavan0Shikhar Daniel1Arun Kumar Dasari2Kaladhar Reddy Aileni3Chaitra patil4Santosh R. Patil5Centre of Molecular Medicine and Diagnostics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, TamilNadu, IndiaDepartment of Oral Medicine and Radiology, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, TamilNadu, IndiaDepartment of Orthodontics, SVS Institute of Dental Sciences, Mahabubnagar, Telangana, IndiaDepartment of Preventive Dentistry, College of Dentistry, Jouf University, Saudi ArabiaDepartment of Oral & Maxillofacial Surgery, Krishnadevaraya College of Dental Sciences & Hospital, Bangalore, IndiaDepartment of Oral Medicine and Radiology, Chhattisgarh Dental College and Research Institute, India; Corresponding author. Department of Oral Medicine and Radiology, Chhattisgarh Dental College and Research Institute, Chhattisgarh, 491441, India.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.http://www.sciencedirect.com/science/article/pii/S2772906024004370Artificial intelligenceOral oncologyPredictionMachine learningDeep learningData preprocessing |
| spellingShingle | Vishnu Priya Veeraraghavan Shikhar Daniel Arun Kumar Dasari Kaladhar Reddy Aileni Chaitra patil Santosh R. Patil Harnessing artificial intelligence for predictive modelling in oral oncology: Opportunities, challenges, and clinical Perspectives Oral Oncology Reports Artificial intelligence Oral oncology Prediction Machine learning Deep learning Data preprocessing |
| title | Harnessing artificial intelligence for predictive modelling in oral oncology: Opportunities, challenges, and clinical Perspectives |
| title_full | Harnessing artificial intelligence for predictive modelling in oral oncology: Opportunities, challenges, and clinical Perspectives |
| title_fullStr | Harnessing artificial intelligence for predictive modelling in oral oncology: Opportunities, challenges, and clinical Perspectives |
| title_full_unstemmed | Harnessing artificial intelligence for predictive modelling in oral oncology: Opportunities, challenges, and clinical Perspectives |
| title_short | Harnessing artificial intelligence for predictive modelling in oral oncology: Opportunities, challenges, and clinical Perspectives |
| title_sort | harnessing artificial intelligence for predictive modelling in oral oncology opportunities challenges and clinical perspectives |
| topic | Artificial intelligence Oral oncology Prediction Machine learning Deep learning Data preprocessing |
| url | http://www.sciencedirect.com/science/article/pii/S2772906024004370 |
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