Medical device recognition that actually works
Standard AI models fail on obscure medical equipment. Our custom vision model — trained on 52,482 curated, validated images across 329 device classes — identifies manufacturers, models, and visible defects that generic AI simply can't see.
Snap a photo. Get instant device identification and defect analysis.
Powered by Google Vertex AI · 94.7% match accuracy · 90%+ manufacturer ID · 85%+ model detection
Built on real-world equipment photos
Not studio shots. Auction listings, warehouse intake photos, and field images — the messy reality the model has to work in.
Why generic AI fails on medical equipment
ChatGPT and Google Vision were never trained on medical device photos. They see a “hospital bed” or a “medical monitor” — not the specific Hill-Rom P3200 Versacare or Philips IntelliVue MX800 that matters for service, parts, and compliance.
Generic AI response
- “This appears to be a hospital bed”
- “This looks like a patient monitor”
- “Medical equipment — type unknown”
Useless for service techs who need exact manufacturer, model, and part compatibility information.
EzBot Vision response
- Manufacturer: Hill-Rom
- Model: P3200 Versacare
- Defects: rust on frame rail, worn casters
Actionable data: find service manuals, compatible parts, check recalls, estimate repair costs.
From scraped chaos to AI precision
Building an accurate medical equipment classifier meant solving problems no one else has tackled. Here's how roughly 300,000 messy auction photos became a precision model.
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Phase 1
Ingestion — the “vacuum”
Aggregated about 300,000 images from public online auction listings into Google Cloud Storage. The challenge: bad titles, missing brands, and stock photos mixed in with real equipment photos.
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Phase 2
The “super tagger” — AI analysis
We didn't trust auction titles. Every image was sent to Gemini 3 with a specialized prompt: identify the manufacturer, model, and any visible damage — rust, cracks, discoloration. Result: 291,000 tagged items.
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Phase 3
The “defect” experiment (v3.0)
First attempt: train strictly on damaged items — rust, cracked screens, dents. Problem: the dataset was too small (about 27,000 images). The model learned to find rust but forgot what normal equipment looked like. High false positives. Lesson learned.
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Phase 4
Expansion and cleaning (v3.1–3.3)
Added 260,000 “clean” images. New problem: the model became lazy — with 90% clean data it always predicted “no defects.” The fix was deep cleaning: synonym merging (zoll_medical to zoll) and junk-tag removal (stock_photo, new_product).
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Phase 5
Precision refinement (v3.4–5.0)
Balanced the class imbalance (no-defect images capped at 15,000; kept 100% of defect images) and used metadata fingerprinting to remove duplicate auction re-postings. The set went from 82,000 to 43,000 images — a 48% cut that sharply increased information density.
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Phase 6
The “gold standard” (v5.1)
Smart balancing: protect rare items (keep 100% if under 200 examples) and sample common items down to 15,000. Final result: 52,482 images, 329 classes, 100% validated labels — production-ready on Google Vertex AI.
Capabilities beyond recognition
The model doesn't just identify devices — it feeds service, procurement, and compliance workflows with data you can act on.
Manufacturer identification
Recognizes 150+ medical device manufacturers including Hill-Rom, Stryker, GE Healthcare, Philips, Siemens, Medtronic, and dozens of obscure brands that generic AI has never seen.
Model & series detection
Goes beyond brand recognition to identify specific model numbers and series variants. Distinguishes a P3200 from a P500, an MX800 from an MX700 — critical for parts compatibility and service procedures.
Visible defect detection
Automatically flags rust, cracked screens, dents, discoloration, worn casters, and other visible damage. Essential for auction buyers evaluating condition and service techs documenting repairs.
Automated action linking
Accurate identification makes the automations possible. One photo instantly links to FDA recalls, owner's manuals, work orders, recent sale prices, warranty status, and service history. The better the ID, the more actions become available.
Real-world applications
Warehouse intake
Snap photos during receiving to auto-populate inventory records.
Field service
Identify unknown equipment on-site to find the correct service docs.
Auction buying
Evaluate condition and identify models before bidding.
Compliance audits
Document equipment conditions with AI-verified records.
The roadmap ahead
The vision model is just the beginning. Here's what we're building next.
Multi-modal fusion
Training a Transformer to read price context ($50 vs $50,000) to improve identification accuracy and detect potential pricing anomalies in auctions.
Visual deduplication
Using image embeddings (vectors) to find duplicate photos even when auction titles are completely different — essential for market analysis and inventory tracking.
Active learning
A human-in-the-loop system where the AI flags low-confidence images for expert review. Accuracy keeps improving with real-world feedback from biomed technicians.
Edge deployment
Shrinking the model to run locally on iPads and phones. Real-time “blurry photo” alerts in the warehouse. Works offline in hospital basements with no WiFi.
Try EzBot Vision
EzBot Vision is in early access. Schedule a quick demo to see how our custom AI identifies medical equipment that generic models can't.
Schedule a demo
See the vision model in action with your own equipment photos.
Get early access
We'll set up your account and train you on best practices.
Start identifying
Upload photos from any device — web, mobile, or API integration.
Free for biomeds and hospitals. No credit card required.
Quick questions
How accurate is the identification?
Our custom model achieves 90%+ accuracy on manufacturer identification and 85%+ on specific model detection — far exceeding generic AI models, which often can't identify medical equipment at all.
What image quality is needed?
The model is trained on real-world auction photos — not studio shots. It handles poor lighting, odd angles, and partial views. If a human can identify it, EzBot Vision probably can too.
Can it identify equipment not in the training data?
For completely new devices, the model identifies the closest match and flags its uncertainty. Our active learning system means new equipment gets added to the model as users submit corrections.
Keep exploring
Meet EzBot AI
Vision is one piece of EzBot — an AI assistant for medical devices backed by 20+ FDA databases and 23M+ records. Web chat is live today: 5 free questions a day, no signup.
Why we build this
In rural Haiti, a failed C-Arm used to mean 8–9 weeks without imaging. With EzBot, a technician had a diagnosis in about 30 minutes. Read the field notes.
One photo. Instant identification.
Chat with EzBot today, or book a demo and bring your own equipment photos.