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

**Authors:** Alhanouf A Alturki, Alanoud F Bin Muammar

PMC · DOI: 10.7759/cureus.104179 · Cureus · 2026-02-24

## TL;DR

This paper explores how AI and patient blood data from electronic health records can predict healing outcomes in dental surgery, enabling more personalized and effective care.

## Contribution

The paper introduces AI-based predictive models using blood biomarkers and EHR data to forecast preoperative healing in dentistry.

## Key findings

- Blood biomarkers like platelet indices and fibrinogen levels are critical for predicting healing outcomes.
- AI algorithms can generate personalized healing risk scores using longitudinal EHR data.
- Digital innovation can optimize the use of autologous blood concentrates like PRF and PRP.

## 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.

## Full-text entities

- **Genes:** FGB (fibrinogen beta chain) [NCBI Gene 2244] {aka HEL-S-78p}
- **Diseases:** AI (MESH:C538142), periodontal disease (MESH:D010510), anemia (MESH:D000740), caries (MESH:D003731), Inflammation (MESH:D007249), nutritional deficiencies (MESH:D044342), infection (MESH:D007239), diabetes (MESH:D003920)
- **Chemicals:** iron (MESH:D007501), vitamin D. (MESH:D014807)
- **Species:** Human immunodeficiency virus 1 (no rank) [taxon 11676], Human immunodeficiency virus (species) [taxon 12721], Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

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## References

20 references — full list in the complete paper: https://tomesphere.com/paper/PMC12959750/full.md

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Source: https://tomesphere.com/paper/PMC12959750