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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](https://www.europarl.europa.eu/topics/en/article/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence), [WHO Ethics Guidelines for AI in Health](https://www.who.int/publications/i/item/9789240029200), and [Google's Responsible AI Practices](https://ai.google/responsibility/responsible-ai-practices/).* | |