Artificial intelligence-enabled prediction of oral squamous cell carcinoma recurrence and treatment efficacy

• Treatment Challenges: Oral squamous cell carcinoma (OSCC) experiences high recurrence rates and treatment failures despite advances in therapies, including immune checkpoint blockade, which suffers from long-term toxicity and resistance issues. • Emerging Therapies: Promising clinical trials are underway for targeted drug therapy and personalized tumor-infiltrating lymphocyte (TIL) therapy, though limited to patients with specific molecular features due to varying clinical outcomes and toxicity. • Artificial Intelligence in Medicine: AI, particularly machine learning (ML), enhances medical tasks with high computational capabilities, playing a critical role in disease diagnosis and prognosis, including in cancer care. • Precision Medicine Advances: AI integrates diverse data sources, such as imaging and genomic data, to refine cancer diagnostics and treatment strategies, leading to better predictive performance in immunotherapy responses. • ML in OSCC Prognosis: ML algorithms are instrumental in predicting OSCC recurrence and therapy outcomes, with studies showing varying successes across different models like decision trees and support vector machines, enhancing the understanding of prognosis and treatment efficacy.