ETHOS Framework
Published Research & AI Trustworthy Healthcare Decision Framework
Problem Context & Objectives
Black-box diagnostic AI models present unacceptable risks when deployed in rural clinical settings without specialist oversight. ETHOS introduces a verifiable, constraint-bound inference framework that combines deep learning with expert rule safety bounds.
Architectural Highlights & Systems Engineering
- Pattern 1
Deterministic Fallback Trees: Hybrid inference engine that automatically downgrades AI recommendations to deterministic clinical decision trees whenever diagnostic confidence drops below ethical thresholds.
- Pattern 2
Explainable Diagnostic Surfaces: Integrated SHAP and LIME visual attribution maps displaying exact clinical indicators responsible for model recommendations.
- Pattern 3
Sub-100ms Micro-Controller Deployment: Optimized model weights for edge execution on micro-server hardware without requiring active internet connectivity.
Technology Stack
Compliance Standards
- WHO Principles for Ethics & Governance of AI for Health
- IEEE Ethical AI Standards
ETHOS Framework Pipeline Blueprint
Deterministic Guardrails
Ethical Safety BoundsHybrid decision engine downgrading AI outputs to WHO-certified clinical decision trees if model confidence falls below 85%.
{
"framework": "ETHOS-v2.1",
"model_confidence": 0.74,
"threshold": 0.85,
"action": "FALLBACK_TO_DETERMINISTIC_TREE",
"active_rule": "WHO_MATERNAL_HYPERTENSION_PROTOCOL_3"
}Measurable Outcomes & Impact
- Outcome #1Published research paper presenting ethical AI deployment methodologies for developing nations.
- Outcome #2Integrated into TotoAfya diagnostic recommendation pipeline as core safety layer.