{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T09:00:48Z","timestamp":1787907648341,"version":"build-2784847793"},"reference-count":227,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T00:00:00Z","timestamp":1680652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Cancer is a problematic global health issue with an extremely high fatality rate throughout the world. The application of various machine learning techniques that have appeared in the field of cancer diagnosis in recent years has provided meaningful insights into efficient and precise treatment decision-making. Due to rapid advancements in sequencing technologies, the detection of cancer based on gene expression data has improved over the years. Different types of cancer affect different parts of the body in different ways. Cancer that affects the mouth, lip, and upper throat is known as oral cancer, which is the sixth most prevalent form of cancer worldwide. India, Bangladesh, China, the United States, and Pakistan are the top five countries with the highest rates of oral cavity disease and lip cancer. The major causes of oral cancer are excessive use of tobacco and cigarette smoking. Many people\u2019s lives can be saved if oral cancer (OC) can be detected early. Early identification and diagnosis could assist doctors in providing better patient care and effective treatment. OC screening may advance with the implementation of artificial intelligence (AI) techniques. AI can provide assistance to the oncology sector by accurately analyzing a large dataset from several imaging modalities. This review deals with the implementation of AI during the early stages of cancer for the proper detection and treatment of OC. Furthermore, performance evaluations of several DL and ML models have been carried out to show that the DL model can overcome the difficult challenges associated with early cancerous lesions in the mouth. For this review, we have followed the rules recommended for the extension of scoping reviews and meta-analyses (PRISMA-ScR). Examining the reference lists for the chosen articles helped us gather more details on the subject. Additionally, we discussed AI\u2019s drawbacks and its potential use in research on oral cancer. There are methods for reducing risk factors, such as reducing the use of tobacco and alcohol, as well as immunization against HPV infection to avoid oral cancer, or to lessen the burden of the disease. Additionally, officious methods for preventing oral diseases include training programs for doctors and patients as well as facilitating early diagnosis via screening high-risk populations for the disease.<\/jats:p>","DOI":"10.3390\/diagnostics13071353","type":"journal-article","created":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T01:10:27Z","timestamp":1680743427000},"page":"1353","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":139,"title":["A Current Review of Machine Learning and Deep Learning Models in Oral Cancer Diagnosis: Recent Technologies, Open Challenges, and Future Research Directions"],"prefix":"10.3390","volume":"13","author":[{"given":"Shriniket","family":"Dixit","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anant","family":"Kumar","sequence":"additional","affiliation":[{"name":"School of Bioscience and Technology, Vellore Institute of Technology, Vellore 632014, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9352-0237","authenticated-orcid":false,"given":"Kathiravan","family":"Srinivasan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology, Vellore 632014, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,5]]},"reference":[{"key":"ref_1","unstructured":"Sankaranarayanan, R., Ramadas, K., Amarasinghe, H., Subramanian, S., and Johnson, N. 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