Knowledge-Guided Large Language Model for Automatic Pediatric Dental Record Understanding and Safe Antibiotic Recommendation
Elevating Pediatric Dental Care with Knowledge-Guided AI
This study introduces a Knowledge-Guided Large Language Model (KG-LLM) framework designed to revolutionize pediatric dental record understanding and ensure safe, evidence-grounded antibiotic recommendations. By integrating a specialized knowledge graph, retrieval-augmented generation (RAG), and a multi-stage safety validation pipeline, KG-LLM addresses critical limitations of traditional clinical decision support systems.
Executive Summary: KG-LLM for Pediatric Dental Care
KG-LLM significantly enhances clinical reliability and interpretability by leveraging structured medical knowledge. It outperforms existing clinical LLMs across multiple benchmarks, demonstrating superior factual correctness, dosage safety, and explanation quality. This robust system represents a meaningful step toward safer and more intelligent antibiotic stewardship in pediatric dentistry.
0%
Reduction in Unsafe Antibiotic Suggestions
0
Improved Record Understanding F1 Score
0
Enhanced Drug-Dose-Duration Accuracy
Deep Analysis & Enterprise Applications
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0.914
Record Understanding F1 Score (KG-LLM)
Knowledge-Guided LLM Architecture
The KG-LLM integrates a pediatric dental knowledge graph, retrieval-augmented generation (RAG), and a multi-stage safety validation pipeline. This architecture enables coherent understanding of multi-modal pediatric dental records and generates clinically interpretable antibiotic recommendations supported by explicit knowledge evidence. It overcomes limitations of general-purpose LLMs which often hallucinate or lack domain-specific knowledge.
Enterprise Process Flow
Pediatric Dental Record Input
→
Record Understanding Module (NER/RE)
→
Knowledge Graph Retrieval
→
LLM Generation & Summarization
→
Safety-Constrained Recommendation Module
→
Safe Antibiotic Recommendation
| Model | Record F1 | Top-1 Acc | CVR (Lower is Better) |
|---|---|---|---|
| ClinicalBERT | 0.812 | 0.641 | 0.124 |
| BioGPT | 0.846 | 0.672 | 0.109 |
| Med-PaLM 2 | 0.874 | 0.708 | 0.091 |
| Llama-2 Clinical (no KG) | 0.867 | 0.716 | 0.084 |
| Proposed KG-LLM (ours) | 0.914 | 0.782 | 0.042 |
The KG-LLM demonstrates superior performance across key metrics, significantly improving record understanding and reducing unsafe antibiotic suggestions compared to strong baselines.
50%
Reduction in Unsafe Antibiotic Suggestions
Dual-Layer Safety Validation
Safety assurance in KG-LLM is achieved through a dual-layer validation mechanism combining deterministic rule checking (for pediatric dosing limits, allergies, drug-drug interactions) with a learned classifier for detecting broader contraindications and dosing errors. This ensures that recommendations adhere strictly to clinical guidelines and patient-specific profiles, significantly reducing the risk of inappropriate prescriptions.
0.042
Contraindication Violation Rate (CVR) - KG-LLM
| Model Variant | Record F1 | CVR | DER | GCS |
|---|---|---|---|---|
| KG-LLM w/o KG | 0.871 | 0.083 | 0.047 | 0.842 |
| KG-LLM w/o Retrieval | 0.884 | 0.067 | 0.036 | 0.861 |
| KG-LLM w/o Causal Safety Module | 0.901 | 0.058 | 0.029 | 0.873 |
| Full KG-LLM (ours) | 0.914 | 0.042 | 0.019 | 0.906 |
Ablation studies confirm that each component, especially the Knowledge Graph, RAG, and Causal Safety Module, contributes substantially to clinical reliability and safety, highlighting their synergistic effect.
Driving Antimicrobial Stewardship
The KG-LLM provides a scalable, transparent, and evidence-grounded framework capable of interpreting complex multimodal clinical records and reducing the risk of inappropriate or unsafe antimicrobial prescriptions in pediatric dentistry. It serves as a foundation for advanced CDSS infrastructures and supports antimicrobial stewardship initiatives.
Enhanced Decision Support
The system offers pediatric dentists a robust AI assistant that can interpret nuanced clinical data, including unstructured notes and radiographic descriptions, to suggest guideline-aligned and patient-specific antibiotic treatments. This reduces human error and promotes best practices.
Scalability and Adaptability
Future work includes exploring strategies to better assess and weight heterogeneous evidence sources and develop mechanisms to ensure timely, reliable knowledge updates. Integrating real-time clinician feedback loops will further enhance reliability and adoption.
Calculate Your AI Transformation ROI
Estimate the potential annual savings and productivity gains your enterprise could achieve by implementing a Knowledge-Guided LLM solution like KG-LLM.
Your AI Implementation Roadmap
A typical enterprise AI adoption journey, adapted for integrating knowledge-guided LLMs in clinical decision support.
Phase 1: Discovery & Knowledge Graph Foundation
Initial consultation to define clinical scope, data sources, and specific pediatric dental use cases. Construction of the domain-specific knowledge graph leveraging existing clinical guidelines, drug databases, and de-identified patient data.
Phase 2: Model Customization & RAG Integration
Fine-tuning of the base LLM (e.g., Llama-3-Med) with pediatric dental datasets. Integration of retrieval-augmented generation (RAG) to dynamically fetch context-specific guidelines and patient history during inference.
Phase 3: Safety Validation & Clinical Alignment
Development and rigorous testing of the multi-stage safety validation pipeline, including rule-based checks and learned classifiers for allergies, contraindications, and dosing. Collaborative validation with pediatric dental experts to ensure clinical accuracy and adherence to standards.
Phase 4: Deployment & Continuous Optimization
Secure deployment within existing clinical decision support systems. Establishment of feedback loops for ongoing model refinement, knowledge graph updates, and performance monitoring to adapt to evolving medical research and patient needs.
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