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EXPLAINABLE ARTIFICIAL INTELLIGENCE THROUGH HEALTHCARE DIAGNOSTICS
Abstract:
As artificial intelligence (AI) continues to revolutionize healthcare diagnostics, there is a growing need for transparency and interpretability in the decision-making processes of AI models. This paper explores the concept of Explainable Artificial Intelligence (XAI) in the context of healthcare diagnostics, focusing on its crucial role in enhancing both the accuracy of diagnoses and the trust between healthcare professionals, patients, and AI systems. We delve into various techniques and methodologies that contribute to creating transparent and interpretable AI models, ensuring that the reasoning behind diagnostic outcomes is accessible and understandable to non-experts. By incorporating XAI into healthcare diagnostics, this research aims to foster a collaborative and informed approach, empowering clinicians and patients to make well-informed decisions based on AI-assisted insights. Through real-world case studies and practical applications, we highlight the benefits of explainability in improving the acceptance and adoption of AI technologies in healthcare, ultimately contributing to more reliable, ethical, and patient-centric diagnostic practices.
Introduction
1.1 Background and Significance
1.2 Objectives of the Study
1.3 Structure of the Paper
Literature Review
2.1 Evolution of Artificial Intelligence in Healthcare
2.2 Current Challenges in Healthcare Diagnostics
2.3 Role of Explainable Artificial Intelligence (XAI)
2.4 Previous Research and Developments
Foundations of Explainable Artificial Intelligence
3.1 Definition and Characteristics of Explainability
3.2 Importance of Explainability in Healthcare Diagnostics
3.3 Ethical Considerations and Regulatory Landscape
Techniques for Achieving Explainability
4.1 Model-Agnostic Approaches
4.1.1 LIME (Local Interpretable Model-agnostic Explanations)
4.1.2 SHAP (Shapley Additive exPlanations)
4.2 Intrinsic Methods
4.2.1 Rule-Based Systems
4.2.2 Attention Mechanisms
Conclusion
5.1 Summary of Key Findings
5.2 Implications for the Future of Healthcare Diagnostics
References
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