🏥 AgentMed: Clinical AI Copilot & Multi-Agent Orchestrator

AgentMed is a reliable clinical AI Copilot. It acts as an automated "second opinion" system, testing how well modern AI models analyze complex medical images like retinal scans.

Instead of relying on a single AI, AgentMed uses a "Maker-Checker" system—where multiple AI models debate a diagnosis to ensure accuracy, safety, and cost-efficiency before handing the final decision to a human doctor.

Live Demo: Hugging Face Space

⚠️ Note on the Live Demo: To make sure you see the AI debate in action, the web demo forces the Second-Opinion AI to review every single scan. It also uses a simple, fixed rule to decide if a scan is safe. To see our smart, cost-saving routing in action, check out the main production code (pipeline.py and consensus_engine.py).

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🚀 Business Impact & Product Value

Building AI for healthcare usually forces a tough choice: use cheap models that might make mistakes, or use expensive, massive models that cost too much to run at scale. AgentMed solves this.

1. Better Accuracy Through AI Teamwork

By forcing two different AI models to double-check ambiguous scans, AgentMed is significantly more accurate than using a single AI on the IDRiD Dataset (455 images evaluated).

• Baseline (Single AI - GPT-4o-mini): 41.98% Accuracy
• AgentMed Pipeline (Llama Maker + GPT Checker): 46.15% Accuracy (+9.9% relative increase)

2. Optimizing API Unit Economics (Doing More with Less)

Running multiple heavy AI models on every single image doubles API costs. AgentMed's routing economics solve this by only running the secondary Checker model on ambiguous cases.

• By intelligently triaging the scans, the system successfully caught and fixed 51 diagnostic hallucinations while only triggering the secondary AI for 37.58% of the workload.
• This reduces overall operational costs compared to a forced dual-inference pipeline.

3. Smart Risk Management (Self-Adjusting Rules)

Instead of hardcoding rules, AgentMed learns over time. If the frontline AI starts acting overly confident about difficult scans, the system automatically tightens its safety net, forcing more scans to get a second opinion. If the AI proves reliable, the system relaxes the rules to save money.

4. Keeping Humans in Control

AgentMed treats AI like a Medical Resident, while the human user remains the Attending Physician.

• Safe Auto-Triage Rate (62.42%): Cases where the AIs agreed with high confidence.
• Human Escalation Rate (37.58%): Cases where the AIs disagreed or were unsure (171 escalations). These are flagged as a "Hard Deferral" and immediately sent to a human doctor.

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🧠 System Architecture

This architecture proves that having AI models check each other's work drastically reduces errors and "hallucinations" in medical tasks.

1. The Primary AI (Maker): Meta's open-source Llama-3.2 looks at the scan first, offering a diagnosis and a confidence score (e.g., "I am 85% sure this is Grade 2").
2. The Second-Opinion AI (Checker): If the Maker's confidence is too low, or if it spots a severe disease, the scan is automatically sent to GPT-4o-mini for a strict audit.
3. Adaptive Safety Net (consensus_engine.py): The system constantly tracks the Maker AI's track record. If the Maker's historical confidence fluctuates, the system dynamically changes the minimum score required to pass without a second opinion.
4. Final Decision: * If both AIs agree and are highly confident ➡️ Green: Safe for Auto-Triage.
   • If the AIs disagree or are guessing ➡️ Red: Hard Deferral to a Human Doctor.

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🛠️ Technical Stack & Implementation Details

• Frontend: Built with Streamlit, optimized to look and feel like a real doctor's dashboard.
• Dynamic LLM Orchestration: A unified API wrapper interfaces seamlessly with both OpenAI (GPT) and NVIDIA (Llama). It utilizes a dynamic Maker-Checker flow that intelligently routes scans to a heavier, secondary AI only when necessary..
• Fault-Tolerant Batch Processing: Built with auto-saving and exponential backoff retry loops. This ensures zero data loss and prevents the pipeline from crashing during network latency or API rate limits.
• Smart Image Compression: Resizes high-resolution biological data in-memory, shrinking file sizes by up to 86% without blurring out tiny, critical details like microaneurysms.

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📂 Repository Structure

• app.py: The live application featuring the Interactive Patient Triage Terminal and Fleet Observability dashboard.
• pipeline.py: The main processing script used to process large batches of images.
• architecture_comparison_report.json: Telemetry and benchmark data proving the cost/accuracy efficacy of the multi-agent system.
• consensus_engine.py: (Smart Safety Net) The brain behind the AI debate. Instead of fixed rules, it constantly monitors the primary AI's track record. If the primary AI becomes too confident on tricky scans, the system tightens the rules, forcing more cases to get a secondary AI opinion. This self-adjusting safety net guarantees patient safety while optimizing costs.
• results_llama_gpt_images.csv: The raw logs of every time the AIs debated a scan.

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🧑‍⚕️ For Clinical Researchers: How to Run a Batch Study

If you are a clinical researcher looking to evaluate a large dataset of patient scans (like a subset of the IDRiD dataset) without writing code, follow this step-by-step guide.

This pipeline will process thousands of images automatically, handle the AI "Maker-Checker" debate, and output a clean CSV file for your statistical analysis.

Step 1: Gather Your Prerequisites

Before running the study, you need two things:

1. Python: Ensure Python is installed on your computer.
2. API Keys (Your Digital ID Badges): You will need access keys to use the AI models.
   • Get an OpenAI API Key (for the GPT-4o Checker).
   • Get an NVIDIA API Key (for the Llama-90B Maker).

Step 2: Prepare Your Data

Create a folder on your computer and place all the retinal scans (.jpg, .png, or .tif) you want to evaluate inside it.

• Example: C:\Users\DrSmith\Research\Retinal_Scans

Step 3: Set Your Environment

Open your computer's Terminal (Mac/Linux) or Command Prompt (Windows) and set your API keys so the script can access them.

For Mac/Linux:

export OPENAI_API_KEY="sk-your-openai-key-here"
export NVIDIA_API_KEY="nvapi-your-nvidia-key-here"

For Windows:

set OPENAI_API_KEY="sk-your-openai-key-here"
set NVIDIA_API_KEY="nvapi-your-nvidia-key-here"

Step 4: Configure the Pipeline Script

Open the pipeline.py file in a text editor (like Notepad or VS Code).

1. Change the Image Path: Find the line IMAGE_DIR = "dataset/train_images/" (usually around line 74).
2. Point it to your data: Change it to the folder where you saved your scans.
   • Example: IMAGE_DIR = "C:\Users\DrSmith\Research\Retinal_Scans"
3. Adjust the Output: Find output_path = "result/result_llama_gpt_large.csv".
   • Change it to: output_path = "C:\Users\DrSmith\Research\Retinal_Scans\results.csv"

Step 5: Run the Evaluation

In your Terminal or Command Prompt (where you set the API keys), run the following command:

python pipeline.py

Step 6: Analyze the Results

Once the script finishes (it might take a while for a large dataset), it will create a file named result_llama_gpt_large.csv. You can open this file with Microsoft Excel or Google Sheets to see the detailed breakdown:

• scan_name: The name of the patient's scan.
• model: Which AI model made the decision.
• model_output: The AI's diagnosis (e.g., "No DR", "Mild DR").
• correct: Whether the AI was right (1) or wrong (0).
• image_size: How big the file was before the AI analyzed it.
• boxes: The AI's "reasoning"—the coordinates where it saw the problem.
• conf: The AI's confidence level (as a percentage).
• is_auto_triage: Whether the system decided it was safe to proceed automatically (1) or if a doctor needed to review it (0).