Boosting Maternal Health: Explainable AI Bridges Trust Gap in Bangladesh
research#xai🔬 Research|Analyzed: Jan 15, 2026 07:04•
Published: Jan 15, 2026 05:00
•1 min read
•ArXiv AIAnalysis
This research showcases a practical application of XAI, emphasizing the importance of clinician feedback in validating model interpretability and building trust, which is crucial for real-world deployment. The integration of fuzzy logic and SHAP explanations offers a compelling approach to balance model accuracy and user comprehension, addressing the challenges of AI adoption in healthcare.
Key Takeaways
- •Hybrid XAI framework (fuzzy-XGBoost) achieved 88.67% accuracy in maternal health risk assessment.
- •Clinician feedback highlighted the value of hybrid explanations, with over 70% preferring them.
- •Healthcare access was identified as the primary predictor by SHAP analysis.
Reference / Citation
View Original"This work demonstrates that combining interpretable fuzzy rules with feature importance explanations enhances both utility and trust, providing practical insights for XAI deployment in maternal healthcare."