Build a Custom AI for Personalized Dental Treatment Plans
AI improves treatment plan accuracy by analyzing patient history, imaging, and clinical data to identify optimal procedures. It increases patient acceptance by generating data-driven options that align with individual needs and insurance coverage.
The system's complexity depends on the variety of data sources. A practice with structured patient records is a direct build. A clinic wanting to integrate unstructured notes, CBCT scans, and intraoral scans requires more sophisticated data processing and feature engineering.
We built a recommendation system for a 7-dentist practice with 10 years of patient records. The AI model, trained on 5,000 anonymized cases, went live in 4 weeks. It reduced plan creation time from 15 minutes to under 2 minutes per patient.
What Problem Does This Solve?
Standard Dental Practice Management Software (PMS) like Dentrix or Eaglesoft offers treatment plan templates, but these are just static checklists. They cannot dynamically adjust a plan based on a patient's bruxism noted in a free-text field or an allergy recorded years ago. The template for a crown is the same for every patient, forcing clinicians to manually override it constantly.
Off-the-shelf AI diagnostic tools like Overjet or Pearl are excellent at identifying caries on radiographs but their function stops there. They diagnose a specific problem but do not synthesize a comprehensive treatment plan. They cannot sequence procedures, factor in insurance limitations, or weigh a bridge versus an implant by analyzing bone density from a 3D scan. They are powerful diagnostic aids, not clinical planning engines.
A multi-specialty group tried to use shared Eaglesoft templates to standardize complex implant cases. When a CBCT scan revealed borderline bone density, the template was useless. The periodontist had to manually create a new plan, message the prosthodontist for review, and wait for a response, causing a 3-day delay for one patient's plan. The attempt at standardization failed because the tool could not handle clinical nuance.
How Does It Work?
We begin by establishing a HIPAA-compliant connection to your Practice Management System, pulling 5 years of anonymized records including ADA codes, clinical notes, and billing data. We use the Python `pydicom` library to parse DICOM files from your CBCT scanner and `trimesh` to process STL files from intraoral scanners, extracting hundreds of potential predictive features for each case.
Using this aggregated dataset, we train a gradient-boosted tree model with XGBoost. This model learns the complex relationships between patient factors and successful treatment outcomes from your own historical data. For unstructured clinical notes, a fine-tuned sentence-transformer model identifies and converts key concepts like 'bisphosphonate use' or 'smoking history' into features the primary model can use.
The final model is wrapped in a FastAPI application and deployed on AWS Lambda. When a clinician requests a plan for a new patient, the system processes all available data and returns a ranked list of treatment options in under 800ms. Each option includes the proposed ADA codes in sequence, a confidence score, and the key factors that influenced the recommendation.
This API is integrated into your workflow via a simple web UI or a browser extension that works with your existing PMS. All activity is logged using `structlog` and monitored with AWS CloudWatch alerts for latency and error rates. The entire production system typically runs for under $50 per month in cloud hosting costs.
What Are the Key Benefits?
From Patient Chart to Plan Options in 90 Seconds
The AI generates a complete, evidence-based treatment plan in under two minutes, eliminating the average 15-minute manual planning time per case.
A Fixed-Price Build, Not a Per-Provider Fee
One scoped project gives you a permanent asset. No recurring SaaS fees that penalize you for adding new associates, hygienists, or locations.
You Own the Data, Model, and Source Code
We deliver the full Python source code and trained model files to your private GitHub repository. You have complete control to modify or extend the system.
Accuracy Monitoring with Automated Alerts
The system tracks plan acceptance rates and sends a Slack notification if performance deviates from the baseline, signaling a need for model retraining.
Works Alongside Your Existing PMS
The recommendation engine connects to Dentrix, Eaglesoft, or Open Dental, pulling data and presenting results without replacing your core clinical system.
What Does the Process Look Like?
Data Extraction and HIPAA BAA (Week 1)
You provide anonymized data exports from your PMS and imaging systems. We sign a Business Associate Agreement and establish a secure data transfer protocol.
Model Training and Validation (Week 2)
We train the AI model on your historical cases. You receive a validation report showing the model's accuracy on predicting successful treatment outcomes.
API Deployment and UI Build (Week 3)
We deploy the core API and build a simple user interface for your clinicians. You get a private URL for testing with select case files.
Clinical Rollout and Handoff (Week 4)
The system goes live for your team. We provide a runbook, full documentation, and a 30-day post-launch monitoring period to ensure smooth adoption.
Frequently Asked Questions
- How much does a custom treatment planning AI cost?
- The cost depends on the number and type of data sources. A system using structured PMS data is a 3-4 week build. Integrating unstructured notes and 3D imaging from CBCT scans extends the timeline to 5-6 weeks. We provide a fixed-price quote after a discovery call where we assess your specific data environment and workflow requirements.
- What happens if the AI gives a bad recommendation?
- The AI provides decision support, but the clinician always makes the final choice. The UI includes a feedback button to flag suboptimal plans. These flagged cases are reviewed and used as negative examples during the next model retraining cycle, continuously improving the system's clinical alignment and preventing repeat errors.
- How is this different from AI diagnostic tools like Pearl or Overjet?
- Diagnostic tools identify pathologies on 2D radiographs, like decay or bone loss. They are a single input. Our system synthesizes multiple inputs, including diagnostics, patient history, 3D scans, and notes, to recommend a full, sequenced treatment plan. We build the logic that connects the 'what' from diagnostics to the 'how' of treatment.
- How do you handle patient data and HIPAA compliance?
- We operate under a strict Business Associate Agreement (BAA). All model training is done on a de-identified dataset you provide. The final system is deployed within your own secure cloud environment, so protected health information never leaves your control. We do not store or access PHI after the initial build is complete.
- Can the model reflect our practice's specific treatment philosophy?
- Yes. The model learns your practice's unique treatment patterns from your historical data. If you have specific protocols, like preferring implants over bridges, that will be reflected in the recommendations. We can also explicitly weigh certain procedures during the training phase to align the AI with your clinical preferences.
- What kind of IT infrastructure do we need to run this?
- You do not need any on-premise servers. The entire system runs on serverless cloud infrastructure, typically AWS Lambda and Supabase for data storage. The only requirement is a modern web browser for your clinical team. We handle the complete cloud setup and deployment as part of the fixed-price build.
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