AI-Generated Dental Treatment Plans: System for Clinics

How AI Automates the Dental Treatment Plan?

A doctor spends 15–25 minutes on a treatment plan: analyzing images, examination data, history, forming a procedure sequence with ICD and ICD-C codes, calculating cost, and creating a patient document. Coding errors lead to insurance denials – up to 3–4 cases per month in large networks. We have developed an AI system that takes over documentation and generates a structured plan based on the doctor's data. This is not a substitute for diagnosis but an assistant that reduces time to 6 minutes and boosts coding accuracy to 94%. The savings per doctor are calculated individually and depend on the clinic's scope.

Why AI Reduces Insurance Denials?

Manual plan creation is tedious, wasting doctor time and creating error risks. The AI system not only speeds up the process but also structures data according to ICD-10-CM and SNODENT (see Wikipedia). Compare:

Result: doctor saves 70% time, insurance accepts the plan first time.

What Data is Needed for Plan Generation?

After an exam, the doctor inputs or dictates: tooth chart, identified pathologies per tooth, patient priorities. The system generates:

• Full treatment plan with procedure sequence
• ICD-10-CM and ICD-C codes for insurance
• Alternative treatment options (conservative vs radical)
• Cost breakdown by stage
• Informed consent for the patient (in plain language)

from langchain_openai import ChatOpenAI from pydantic import BaseModel from typing import Optional import json class ToothCondition(BaseModel): tooth_number: int # по ISO 3950 diagnosis: str severity: str # mild / moderate / severe priority: str # urgent / planned / cosmetic class TreatmentPlan(BaseModel): patient_id: str chief_complaint: str diagnoses: list[ToothCondition] treatment_phases: list[dict] # [{phase, procedures, duration_weeks, cost_range}] total_visits_estimate: int contraindications: list[str] alternative_options: list[dict] informed_consent_summary: str class DentalTreatmentPlanGenerator: SYSTEM_PROMPT = """Ты — AI-ассистент стоматолога. Помогаешь формализовать план лечения. Ты НЕ ставишь диагноз — ты структурируешь данные, предоставленные врачом. Используй актуальные стандарты: МКБ-10-СМ, МКБ-С (SNODENT), СанПиН 2.1.3.2630-10. Последовательность процедур должна соответствовать клинической логике: сначала неотложная помощь → гигиенические процедуры → терапия → хирургия → ортопедия.""" def __init__(self): self.llm = ChatOpenAI(model="gpt-4o", temperature=0.1) def generate_plan( self, patient_data: dict, tooth_conditions: list[ToothCondition], patient_preferences: dict ) -> TreatmentPlan: conditions_text = "\n".join([ f"Зуб {tc.tooth_number}: {tc.diagnosis} ({tc.severity}), приоритет: {tc.priority}" for tc in tooth_conditions ]) prompt = f"""Создай план стоматологического лечения. Данные пациента: - Возраст: {patient_data.get('age')} - Аллергии: {patient_data.get('allergies', 'не указаны')} - Системные заболевания: {patient_data.get('systemic_conditions', 'нет')} - Принимаемые препараты: {patient_data.get('medications', 'нет')} - Главная жалоба: {patient_data.get('chief_complaint')} Состояние зубов (по данным врача): {conditions_text} Предпочтения пациента: - Бюджет: {patient_preferences.get('budget', 'не ограничен')} - Приоритет: {patient_preferences.get('priority', 'качество')} (качество/скорость/бюджет) - Страховка: {patient_preferences.get('insurance', 'нет')} Создай план с: 1. Этапы лечения (фазы с обоснованием последовательности) 2. Для каждой процедуры: название, код МКБ-С, количество посещений, риски 3. Альтернативный план (более консервативный) 4. Предупреждения и противопоказания 5. Краткое резюме для пациента (без медицинского жаргона) Верни JSON структуры TreatmentPlan.""" response = self.llm.invoke([ {"role": "system", "content": self.SYSTEM_PROMPT}, {"role": "user", "content": prompt} ]) return TreatmentPlan.model_validate_json(response.content) 

How AI Analyzes X-Ray Images?

For clinics with digital X-rays—data extraction via Vision API. The model describes changes on panoramic images: carious cavities, periapical changes, bone loss. The result is used as auxiliary information for the doctor.

import base64 from openai import OpenAI client = OpenAI() def analyze_dental_xray(image_path: str) -> dict: """Анализирует рентгеновский снимок — вспомогательно для врача""" with open(image_path, "rb") as f: image_b64 = base64.b64encode(f.read()).decode() response = client.chat.completions.create( model="gpt-4o", messages=[{ "role": "user", "content": [ {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_b64}"}}, {"type": "text", "text": """Опиши видимые изменения на панорамном рентгеновском снимке зубов. Структурируй по зонам. Укажи: кариозные полости, периапикальные изменения, потеря костной ткани, состояние корневых каналов. ВАЖНО: Это вспомогательная информация для врача, не диагноз."""} ] }], max_tokens=500 ) return {"xray_observations": response.choices[0].message.content} 

How Does It Integrate with MIS?

We connect to 1C:Meditsina, Dental4Windows, Ident. The plan is loaded as scheduled procedures linked to the patient, with status "planned" and ICD-C code.

# Интеграция с 1С:Медицина, Dental4Windows, Ident class DentalMISConnector: def push_treatment_plan(self, plan: TreatmentPlan, mis_patient_id: str): """Загружает план в медицинскую информационную систему""" procedures = [] for phase in plan.treatment_phases: for proc in phase["procedures"]: procedures.append({ "code": proc["icds_code"], "name": proc["name"], "tooth_number": proc.get("tooth_number"), "phase": phase["phase_number"], "estimated_cost": proc.get("cost_range"), "status": "planned" }) return self.mis_client.create_treatment_plan( patient_id=mis_patient_id, procedures=procedures, created_by="ai_assistant" ) 

What's Included in the Work

The project includes:

• Basic plan generator on GPT-4o with custom prompt and validation pipeline
• Integration with MIS (1C:Meditsina, Dental4Windows, Ident) via REST API
• X-ray analysis module via Vision API
• Data schema configuration for your standards and encoders
• 2-day training for doctors and administrators
• 3 months of technical support

Our Experience and Results

We have implemented over 10 AI projects in healthcare, including for a network of 8 dental clinics. The team has 5+ years in MLOps and NLP. We guarantee coding accuracy of at least 90% at start (94% based on our data). We use certified OpenAI models and our own fine-tuned models for clinic specifics.

Case study: a network of 8 dental clinics. Average time to create a treatment plan: 22 min → 6 min (doctor reviews and corrects AI draft). ICD-C code match accuracy (checked by insurance department): 94%. First 4 months: 0 insurance denials due to incorrect coding (previously 3–4 per month).

Timeline: basic plan generator: 3–4 weeks; integration with MIS and X-ray analysis: an additional 6–8 weeks.

Contact us for a demo. Request a pilot project—we will evaluate your infrastructure and prepare a proposal.