Bioinformatics in Dentistry: Preoperative Healing Prediction Using Artificial Intelligence and Patient Blood Profiles in Electronic Health Records

Abstract

Bioinformatics and artificial intelligence (AI) have become a new direction in dentistry, enabling predictive, precision-focused surgical care. In oral surgery and dental implant procedures, postoperative healing outcomes are inconsistent across most cases. These outcomes are affected by systemic biological determinants that are often documented in electronic health records (EHRs) but are seldom used to forecast risk during preoperative care. This literature review focuses on the emerging role of AI-based predictive models that use blood biomarker profiles and nutritional information from EHRs to predict preoperative healing outcomes. Specific attention is paid to blood-based elements (platelet indices, leukocyte counts, fibrinogen levels, hemoglobin status, and micronutrient markers), which play critical roles in inflammation, angiogenesis, and tissue regeneration. The article also discusses how AI algorithms, such as machine learning and gradient-boosting methods, can use longitudinal EHR data to create personalized healing risk scores and automate the optimal utilization of autologous blood concentrates, including platelet-rich fibrin (PRF) and platelet-rich plasma (PRP). The results endorse a paradigm shift toward predictive, data-driven dentistry that maximizes natural healing prowess through digital innovation.

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