Explainable AI: Ethical Frameworks, Bias, and the Necessity for Benchmarks
Topic overview
This review examines explainable AI (XAI) in pediatric surgery, addressing critical challenges of algorithmic bias, transparency, and trust in AI-driven clinical decisions for vulnerable pediatric populations. The authors emphasize the need for ethical frameworks and standardized benchmarks to ensure safe, fair AI implementation in children's healthcare.
Key takeaways
- XAI makes AI decisions interpretable and accountable, addressing opacity concerns in pediatric healthcare applications.
- Bias in AI models poses significant risks to vulnerable pediatric populations requiring careful ethical oversight.
- Standardized benchmarks are essential for evaluating XAI safety and effectiveness in pediatric surgery contexts.
- Trust and transparency in AI systems depend on explainability frameworks tailored to clinical decision-making needs.
- Ethical frameworks must guide XAI development to ensure fair and safe AI applications for children.
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How to cite: GlobalCastMD. Explainable AI: Ethical Frameworks, Bias, and the Necessity for Benchmarks. GlobalCastMD Medical Library. 2025-09-23. https://library.globalcastmd.com/article/11034
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