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Beck_Moulton
Beck_Moulton

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Stop Taking the Wrong Pills: Building a Vision Pro AR Med-Agent with Gemini 1.5 Pro

#python #ai #tutorial #opensource

Have you ever stared at a cabinet full of prescription bottles, wondering if taking that allergy pill with your cold medicine will turn your heart into a percussion instrument? You’re not alone. Medication errors and dangerous drug-drug interactions (DDIs) are a massive global health challenge.

In this tutorial, we are diving deep into Spatial Computing, Vision Pro development, and Multimodal AI. We’ll build a "Medication Safety Agent" that leverages Gemini 1.5 Pro Vision to identify medicine boxes and loose capsules in real-time, cross-referencing them against a contraindication database to provide life-saving Augmented Reality healthcare alerts. This is "Learning in Public" at its finest—let’s turn pixels into protection!

The Architecture: From Vision to Warning

Building for VisionOS requires a tight loop between the camera feed (via WorldTracking), the multimodal LLM for reasoning, and RealityKit for the spatial overlay.

Here is how the data flows from your pill bottle to your retinas:

graph TD
    A[Vision Pro Camera Feed] -->|Capture Frame| B(SwiftUI ARView)
    B -->|Image Data + Prompt| C{Gemini 1.5 Pro Vision}
    C -->|Identify Drug + Dosage| D[Drug Interaction Engine]
    D -->|Check Contraindications| E{Hazard Detected?}
    E -- Yes --> F[RealityKit 3D Warning Overlay]
    E -- No --> G[RealityKit Green Safety Badge]
    F --> H[User's Spatial Field]
    G --> H
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Prerequisites

To follow along with this advanced build, you'll need:

  • VisionOS 2.0+ & Xcode 16.
  • Gemini 1.5 Pro API Key (for that massive 1M+ token context window).
  • RealityKit & SwiftUI knowledge.
  • A healthy dose of curiosity.

Step 1: Capturing the Spatial Context

In VisionOS, we don't just "take a screenshot." We want to capture the user's focus. Using RealityKit, we can attach identifiers to detected planes or objects. For simplicity, we'll use a ViewAttachment to anchor our UI to the detected pill bottle.

import SwiftUI
import RealityKit
import ARKit

struct MedScannerView: View {
    @State private var analysisResult: String = "Scanning..."

    var body: some View {
        RealityView { content, attachments in
            // Setup persistent world tracking
            if let scene = try? await Entity(named: "Scene", in: realityKitContentBundle) {
                content.add(scene)
            }
        } update: { content, attachments in
            // Update UI based on AI feedback
            if let uiEntity = attachments.entity(for: "warning_label") {
                // Logic to position UI near the detected bottle
                content.add(uiEntity)
            }
        } attachments: {
            Attachment(id: "warning_label") {
                AlertCard(message: analysisResult)
            }
        }
    }
}
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Step 2: The Brains - Gemini 1.5 Pro Vision

Why Gemini 1.5 Pro? Because it excels at OCR (Optical Character Recognition) on curved surfaces (like medicine bottles) and understands visual features of capsules (colors, imprints).

We’ll send the frame to Gemini with a system prompt that enforces a JSON response for our safety logic.

func analyzeMedication(image: UIImage) async {
    let prompt = """
    Identify the drug name and dosage from this image. 
    Check for interactions with: [User_Current_Meds_List].
    Return JSON: {"drug_name": "...", "risk_level": "high/low", "reason": "..."}
    """

    // Using GoogleGenerativeAI SDK
    let model = GenerativeModel(name: "gemini-1.5-pro", apiKey: API_KEY)
    let response = try? await model.generateContent(image, prompt)

    DispatchQueue.main.async {
        self.analysisResult = response?.text ?? "Analysis failed"
    }
}
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Step 3: Implementing the "Safety Check" Logic

For a production-grade app, you wouldn't just trust the LLM’s internal knowledge for medicine. You’d use the LLM to extract the Drug Name, then query a verified medical API (like RxNav).

💡 Pro-Tip: For more production-ready examples and advanced patterns in AI-integrated VisionOS apps, I highly recommend checking out the technical deep-dives at WellAlly Blog. They cover the intricacies of HIPAA-compliant data handling in vision systems that we're only scratching the surface of here.

Step 4: Spatial UI with SwiftUI

When Gemini identifies a "High Risk" interaction (e.g., mixing Warfarin with Aspirin), we need a UI that demands attention but doesn't induce panic.

struct AlertCard: View {
    let message: String

    var body: some View {
        VStack(spacing: 12) {
            Image(systemName: "exclamationmark.triangle.fill")
                .font(.system(size: 40))
                .foregroundColor(.red)

            Text("Drug Interaction Alert")
                .font(.extraLargeTitle)

            Text(message)
                .multilineTextAlignment(.center)
                .font(.headline)
        }
        .padding(30)
        .glassBackgroundEffect() // The iconic VisionOS look!
    }
}
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Wrapping Up: The Future of Ambient Computing

We've just built a prototype that turns a $3,500 headset into a life-saving medical assistant. By combining Gemini 1.5 Pro's multimodal reasoning with VisionOS's spatial anchoring, we create an "Ambient Agent" that watches over the user.

Key Takeaways:

  1. Context is King: Using Vision to identify the exact pill box is safer than manual entry.
  2. Multimodal beats OCR: Gemini understands the intent and danger, not just the text.
  3. Spatial Feedback: Anchoring warnings directly to the object prevents confusion.

Are you building for Vision Pro or experimenting with Multimodal AI? Drop a comment below! I’d love to see how you’re pushing the boundaries of spatial interfaces.

If you enjoyed this tutorial, don't forget to ❤️ and save it! For more advanced architectural patterns in healthcare tech and AI, head over to wellally.tech/blog.

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