Oral cavity and oropharyngeal cancers continue to pose serious health concerns in the U.S. In 2024, the National Cancer Institute estimated approximately 58,450 new cases and 12,230 deaths, reflecting an overall incidence of about 11 per 100,000 persons, with highest rates among individuals aged 75–84 cancer.gov. According to the NIH, between 2015–2019 the overall incidence was 11.5 per 100,000, with significantly higher rates in men (17.4) than women (6.4); rates notably increase after age 50 and peak over age 65 (43.7–44.8) nidcr.nih.gov. Diagnosis typically occurs in the 60s to 70s, and 60% of cases are regional or metastatic at detection, contributing to a 5‑year survival rate of ~69% cancer.gov.

Key Risk Factors & Population Disparities

Major risk contributors include tobacco, alcohol, and HPV infection, with HPV-related oropharyngeal cancers contributing to an annual incidence increase of ~1% since the mid-2000s arxiv.org+5cancer.gov+5en.wikipedia.org+5. Survival declines dramatically with late-stage diagnosis: over 80% for localized disease, ~39% for metastatic . Notably, while oral cancer affects all races, incidence varies: in 2015–2019, incidence per 100,000 among White males was 20.3, compared to 13.2 for Black males and 10.4 for Hispanic males nidcr.nih.gov.

AI & Deep Learning: Shaping Early Detection and Prognosis

AI-driven tools, particularly deep learning (DL) and machine learning (ML), are showing exceptional promise:

• A 2024 BMC Oral Health systematic review covering 54 DL studies found diagnostic accuracies ranging from 85% to 100%, with F1 scores between 79–89%, and pooled diagnostic odds ratio of 2549—indicating robust performance bmcoralhealth.biomedcentral.com+1bmcoralhealth.biomedcentral.com+1.

• A 2025 Frontiers narrative review emphasized computer vision models trained on clinical photographs for detecting oral cancers and potentially malignant disorders (OPMDs), underscoring AI’s clinical utility bmcoralhealth.biomedcentral.com+6frontiersin.org+6frontiersin.org+6.

• A meta-analysis of 14 DL-based detection systems reported pooled sensitivity of 86% and specificity of 67% for malignant lesion classification pubmed.ncbi.nlm.nih.gov.

• In advanced image diagnostics: a 2024 study using U‑Net with ResNet‑34 achieved high precision in segmentation/classification; authors noted the need for further validation in early lesions and real-world deployment bmcoralhealth.biomedcentral.com.

• Cutting-edge research (June 2025) demonstrated neural networks achieving 93.6% accuracy in classifying oral cancers using dimensionality reduction .

AI's Broader Benefits & Implementation Challenges

• Equity through telehealth: AI-powered apps, smartphone interfaces, and tele-dentistry tools can screen underserved or rural populations, compensating for lack of specialist access frontiersin.org.

• Real-world caveats: Many AI models rely on limited or homogeneous datasets, reducing generalizability; ethical/regulatory oversight (e.g., HIPAA, CONSORT‑AI guidelines) is needed for transparency and fairness iopscience.iop.org+4frontiersin.org+4bmcoralhealth.biomedcentral.com+4.