Bridging the Trust Gap: Clinician-Validated Hybrid Explainable AI for Maternal Health Risk Assessment in Bangladesh
PositiveArtificial Intelligence
- A study has introduced a hybrid explainable AI (XAI) framework for maternal health risk assessment in Bangladesh, combining ante-hoc fuzzy logic with post-hoc SHAP explanations, validated through clinician feedback. The fuzzy-XGBoost model achieved 88.67% accuracy on 1,014 maternal health records, with a validation study indicating a strong preference for hybrid explanations among healthcare professionals.
- This development is significant as it addresses the critical barrier of explainability and trust in AI applications within resource-constrained healthcare settings, potentially improving maternal health outcomes in Bangladesh.
- The integration of explainable AI in healthcare reflects a growing recognition of the need for transparent AI systems, particularly in high-stakes environments. This aligns with ongoing discussions about the importance of reliable metrics for AI explainability and the role of human expertise in interpreting AI-generated insights.
— via World Pulse Now AI Editorial System
