R&D, manufacturing, clinical, and compliance - 33-slide playbook
Build AI workflows across devices, clinical, and manufacturing without code.
Directly connect CBCT, intraoral scanners, CAD/CAM, and production equipment.
Private deployment secures medical data and supports HIPAA/GDPR and ISO 13485.
Plugin ecosystem × workflow engine × industry asset library = oral medtech-specific solution
Bonus: Cross-industry AIOps case (Michelin)
import pydicom
from PIL import Image
import numpy as np
def main(file_path: str) -> dict:
# 1. Read DICOM
ds = pydicom.dcmread(file_path)
# 2. Extract Clinical Meta
meta = {
"modality": ds.Modality, # DX/CT
"exposure": f"{ds.KVP}kV",
"patient_id": ds.PatientID
}
# 3. Convert to Image for Vision Model
# Apply Windowing (VOI LUT)
img_array = apply_voi_lut(ds.pixel_array, ds)
return {
"meta": meta,
"image": to_base64(img_array)
}// Generated by DIFY
Title: Initial Oral Health Report
Patient: Mr. Zhang (ID: 88392)
Findings:
1. HIGH RISK Interproximal caries on upper left first molar (#26), depth near the pulp.
2. MEDIUM Calculus accumulation in the lower anterior region.
Suggestion:
Prioritize treatment for #26 caries (resin filling) and schedule full-mouth scaling.
Orthodontic treatment spans a long cycle with fluctuating compliance.
Underwearing or poor fit at home is hard to detect promptly.
By the next visit (about 2 months later), it is often too late.
Delayed feedback impacts treatment progress and outcome evaluation.
Patients take three photos weekly with a cheek retractor (front/side).
DIFY retrieves the 3D simulation for the current stage (STL render).
Detects "air gap" spacing.
Greater than 1 mm indicates tracking off.
Automatically sends WhatsApp/SMS reminders to use chewies.
role: Ortho_Monitor
task: Compare current_photo vs target_render
checklist:
aligner_fit:
instruction: "Check for space between tooth edge and aligner."
threshold: "1 mm"
attachments:
instruction: "Are all composite buttons visible?"
hygiene:
instruction: "Check for red/swollen gums."
output_format:
status: "GO" | "NO-GO"
message: "Friendly feedback to patient."
"Hi Alice! DIFY AI noticed a small gap on your upper incisors. 🦷
Action: Please use your chewies for 20 mins tonight.
No need to come in! See you next week."
Holes, distorted margins
Fit deviation requiring rework
Rework cost: $50 + 3-day delay
import trimesh
def check_stl(file_path):
mesh = trimesh.load(file_path)
# 1. Check for Holes (Watertight)
is_watertight = mesh.is_watertight
# 2. Check Resolution
face_count = len(mesh.faces)
# 3. Decision
if not is_watertight:
return {"status": "FAIL", "reason": "Mesh has holes"}
if face_count < 10000:
return {"status": "FAIL", "reason": "Low resolution"}
return {"status": "PASS"}Incidents and manual checks kept rising despite mature monitoring.
DIFY + MCP quickly connected ServiceNow and ops tools.
Prototypes first, then approval via governance and data classification.
Author Matt Saunders | Translator Liu Yameng | Planner Ding Xiaoyun
Monitoring was mature, yet incidents and manual checks kept rising.
DBA/K8s bots connected to ServiceNow in hours.
KPI impact was hard to quantify; MTTR pressure and staffing fears surfaced.
Low-code exploration to codify ops experience into reusable workflows.
Data classification + governance alignment, focus on two flagship use cases.
ServiceNow | GitHub | Alibaba Cloud resources
Deployed in a validated Alibaba Cloud landing zone with existing security controls.
MCP servers available: 5,800+
"We want to safely and cost-effectively test whether AIOps can ease pain in one or two areas.
Success means learning what works, what doesn't, and building reusable patterns."
- Matthew Liu
Before any data leaves the clinic server (on-prem) for cloud LLMs, it must be de-identified.
text = "Patient John Doe (DOB: 1980-01-01)..."
# Redacted
text = "Patient [NAME] (DOB: [DATE])..."| Time | User | Action | Status |
|---|---|---|---|
| 10:00:01 | Dr. Smith | Viewed Report #882 | SUCCESS |
| 10:05:23 | System | AI Analysis #883 | SUCCESS |
| 10:15:00 | Nurse Joy | Export Patient Data | DENIED |
Key Takeaway: Transform diagnostic efficiency while maintaining Dentsply Sirona ecosystem compatibility
Key Takeaway: Streamline digital workflows from design to production with AI-powered quality gates
3D smile simulation in under 5 minutes with treatment duration estimates
"All-in-one communication tool" for patient understanding enhancement
AI-powered aligner fit detection and automated patient nudges
Key Takeaway: Increase case acceptance through visual communication and continuous patient engagement
Key Takeaway: Maintain regulatory compliance while reducing documentation burden through AI automation
AI-powered implant and component detection
Smart inventory triggers and vendor integration
Reduce stockouts and minimize carrying costs
Inventory Carrying Cost Reduction
Stockout Incidents Reduction
Annual Savings per Practice
Key Takeaway: Transform reactive inventory management into predictive, AI-driven supply intelligence
Co-creating a new standard for intelligent oral medtech
