Developing Transparent AI Models to Enhance Interpretability and Trust in Medical Diagnostics: Implementing explainable AI techniques to provide transparent explanations for medical diagnoses, enhancing trust and acceptance among healthcare professionals
Published 16-09-2024
Keywords
- Explainable AI,
- Trust
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Abstract
Explainable artificial intelligence (XAI) plays a crucial role in enhancing trust and interpretability in medical diagnosis, a domain where decision-making is highly sensitive and consequential. This paper explores the implementation of XAI techniques to provide transparent explanations for medical diagnoses, aiming to improve trust among healthcare professionals and patients. By integrating XAI into medical decision support systems, we address the need for interpretable AI models, which are essential for fostering trust, understanding complex medical decisions, and ultimately improving patient outcomes. Through case studies and analysis, we demonstrate the effectiveness of XAI in enhancing interpretability and trust in medical diagnosis.
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References
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