# Machine-Learning Applications in Oral Cancer: A Systematic Review
###### Abstract  
Over the years, several machine-learning applications have been suggested to assist in various clinical scenarios relevant to oral cancer. We offer a systematic review to identify, assess, and summarize the evidence for reported uses in the areas of oral cancer detection and prevention, prognosis, pre-cancer, treatment, and quality of life. The main algorithms applied in the context of oral cancer applications corresponded to SVM, ANN, and LR, comprising 87.71% of the total published articles in the field. Genomic, histopathological, image, medical/clinical, spectral, and speech data were used most often to predict the four areas of application found in this review. In conclusion, our study has shown that machine-learning applications are useful for prognosis, diagnosis, and prevention of potentially malignant oral lesions (pre-cancer) and therapy. Nevertheless, we strongly recommended the application of these methods in daily clinical practice. oral cancer; OSCC; machine learning; applications +
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the information provided by the complementary tests. In short, an accurate diagnosis is obtained after analyzing the data of the disease. Artificial intelligence (AI) is commonly employed among diverse areas of medicine [7; 8]. Radiology and sophisticated imaging technologies, pathology, ophthalmology, and dermatology are the disciplines to which it has made significant contributions [7; 8]. In each case, a number of impediments must be assessed [8]. Three factors are commonly used to apply the regulations: the danger to patient safety, the presence of a predictive algorithm, and the amount of human involvement [8]. Mathematical models have been used to analyze specific aspects related to oral cancer. Multistage clonal expansion models have been used to analyze the incidence of human papillomavirus (HPV)-related and unrelated oral squamous cell carcinoma, concluding that this model can be useful to identify temporal trends in cancer mechanisms [9]. Additionally, mathematical modeling combined with vitro experimentation has been used to analyze the nanoparticle uptake of oral cancer cells, concluding that the number of receptors per cell was the dominant mechanism in the process [10]. Machine learning (ML) has also been used in oral cancer studies to explore the discrimination between well-differentiated (WD) oral squamous cell carcinoma (OSCC) and moderately or poorly differentiated OSCC [11], to evaluate its ability to predict disease outcome [12], to predict the occurrence of lymph node metastasis of early-stage oral tongue squamous cell carcinoma [13], among other topics of this disease. ML applications can be classified based on the clinical context of the disease, including diagnosis and prevention, prognosis, potentially malignant oral lesions (pre-cancer), and therapy and quality of life. According to the NCI Dictionary of Cancer Terms ([[https://www.cancer.gov/publications/dictionaries/cancer-terms](https://www.cancer.gov/publications/dictionaries/cancer-terms)]([https://www.cancer.gov/publications/dictionaries/cancer-terms](https://www.cancer.gov/publications/dictionaries/cancer-terms)), accessed on 1 March 2022), diagnosis corresponds to the process of identifying a disease, condition, or injury from its signs and symptoms. Cancer prevention includes avoiding risk factors and increasing protective factors. Prognosis is the likely outcome or course of a disease, i.e., the chance of recovery or recurrence. Potentially malignant oral lesions are states of the oral mucosa that are at an increased risk of malignant transformation compared to healthy mucosa [14]. Therapy and quality of life represent the clinical approach to cancer and how it affects an individual's sense of well-being and ability to carry out activities of daily living.