Artificial Intelligence-Driven Pharmacotherapy in Post-Operative Oral and Maxillofacial Surgery: Transforming Prescription Practices

Postoperative pain, infection, and medication-related complications remain significant challenges following oral and maxillofacial surgery (OMFS).The prescription of appropriate analgesics, antibiotics, and supportive medications must navigate complex patient histories, potential drug-drug interactions (DDIs), and individual risk factors.Artificial intelligence (AI), particularly machine learning (ML) and large language models (LLMs) is emerging as a powerful clinical decision-support tool for optimizing postoperative pharmacotherapy.This article examines current applications of AI in predicting medication order errors, detecting clinically significant DDIs, identifying patients at risk for medication-related osteonecrosis of the jaw (MRONJ), and accelerating drug discovery for regenerative therapies.The evidence demonstrates that while AI tools achieve high sensitivity in DDI detection and can accurately predict voided medication orders, significant limitations remain, including false-positive alerts, missed critical interactions, and the black-box nature of deep learning models.The near future will see AI integrated as a supervised decision-support system, augmenting rather than replacing the oral surgeon's clinical judgment.