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⚠️ ETHICS.md — PneumoOps Responsible AI Policy
This document is a mandatory part of the PneumoOps project and must be read before using or contributing to this system.
🚨 1. NOT A Medical Device
❌ This tool does NOT detect disease with medical certainty. ✅ This tool assists in prediction as an educational MLOps demonstration.
PneumoOps is built for academic and research purposes only. It is a demonstration of MLOps concepts — A/B testing, drift monitoring, and continuous deployment — using chest X-ray classification as an applied example.
This system is NOT:
- A licensed medical device
- A substitute for professional clinical diagnosis
- Validated for use in patient care
Every prediction must be treated as:
- Potentially incorrect
- Requiring review by a qualified medical professional
- An educational output, not a clinical recommendation
🔐 2. Data Privacy & Consent
What This System Does With Your Images
- Images uploaded to this tool are processed in-memory for inference only
- No images are permanently stored on the server by default
- No user-identifiable information (name, date of birth, patient ID, scan metadata) is collected, logged, or retained
- Prometheus metrics log aggregate statistics only (prediction class counts, latency) — never individual images or identities
What You Must NOT Upload
- X-rays containing embedded patient metadata (DICOM tags)
- Images with visible patient names, dates, or hospital identifiers
- Any image you do not have the right to use
If Data Collection Is Enabled (Optional Fine-Tuning Mode)
If the PNEUMOOPS_COLLECT_DATA=true environment variable is set:
- A text-based consent notice is shown to the user before submission
- Only the anonymized image (stripped of metadata) and its prediction JSON are saved
- Data is stored locally and never transmitted to third parties
- Users can opt out by not submitting their image
⚖️ 3. Bias & Accuracy Limitations
This model was trained on ChestMNIST, a research dataset with known limitations:
| Limitation | Risk |
|---|---|
| Small image size (224×224, grayscale) | May miss subtle findings visible on full-resolution clinical scans |
| Dataset demographic bias | Performance may vary across different patient populations, scanner hardware, and imaging protocols |
| Class imbalance | Rare conditions (Hernia, Pneumonia) have very low F1 scores (0.0) due to insufficient training examples |
| No temporal data | The model sees single frames only — cannot account for disease progression |
| No radiologist validation | Predictions have NOT been validated by clinical experts |
Macro AUROC of 0.808 is a research-grade metric. It does not translate to clinical accuracy, sensitivity, or specificity at a diagnostic threshold.
🧠 4. Responsible AI Language Policy
All communication from this system must follow these rules:
✅ Use This Language
- "The model predicts a possible finding of..."
- "This assists in identifying potential..."
- "Confidence score indicates a statistical likelihood of..."
- "Results should be reviewed by a qualified clinician."
❌ Never Use This Language
- "This patient has pneumonia."
- "The model detects disease with X% accuracy."
- "This result confirms a diagnosis of..."
- "No disease found." (absence of prediction ≠ absence of disease)
🔄 5. Model Drift & Retraining Obligations
The drift monitoring system exists for a reason. When DRIFT_DETECTED is flagged:
- The incoming image is statistically different from training data
- Predictions on out-of-distribution data are unreliable
- A human reviewer should flag this image
- Retraining should be considered if drift is systematic
Ignoring persistent drift alerts in a production clinical system would be an ethical failure.
👥 6. Attribution & Accountability
| Role | Responsibility |
|---|---|
| Developers | Ensure model limitations are clearly communicated |
| Operators | Never deploy without visible disclaimers |
| Users | Never use predictions to make unsupervised clinical decisions |
| Evaluators | Judge on MLOps pipeline quality, not clinical validity |
📋 7. Compliance Acknowledgment
By using, deploying, or contributing to PneumoOps, you acknowledge that:
- You have read this document in full
- You understand this is an educational tool, not a medical device
- You will not use predictions as a substitute for clinical judgment
- You will not upload images containing patient PII
- You accept responsibility for any use of this system
This ethics policy was authored as part of the PneumoOps academic MLOps project. It follows principles from the EU AI Act, WHO Ethics Guidelines for AI in Health, and Google's Responsible AI Practices.