Bridging the Interpretability Gap: A Systematic Analysis of Explainable Artificial Intelligence (XAI) and Generative Models in Precision Medicine and Healthcare Analytics
Keywords:
Precision Medicine, Explainable AI (XAI), Generative Adversarial Networks, Healthcare DisparitiesAbstract
Background: The rapid integration of Artificial Intelligence (AI) into healthcare has revolutionized diagnostic precision and treatment personalization. However, the adoption of complex "black box" algorithms, particularly Deep Learning models, faces significant hurdles regarding interpretability, trustworthiness, and ethical bias.
Objectives: This study provides a systematic analysis of the current state of AI in biomedicine, focusing specifically on the pivotal role of Explainable Artificial Intelligence (XAI) and Generative AI models. The primary objective is to evaluate how interpretability mechanisms can reconcile the trade-off between algorithmic performance and clinical transparency.
Methods: We conducted a comprehensive theoretical analysis of recent literature, examining data sharing initiatives, synthetic data generation using Generative Adversarial Networks (GANs), and the application of Large Language Models (LLMs). We utilized a taxonomy of interpretability to assess various XAI frameworks, including SHAP, LIME, and counterfactual explanations, against clinical requirements for accountability.
Results: The analysis indicates that while deep learning offers superior predictive capabilities in precision medicine, its opacity remains a barrier to deployment. The results demonstrate that synthetic data generation via cGANs effectively preserves patient privacy while expanding training datasets. Furthermore, XAI methods are critical for identifying systemic biases in training data, though current evaluation metrics for these explanations often lack standardization.
Conclusions: To realize the full potential of AI in healthcare, systems must transition from opaque prediction engines to transparent decision-support partners. The integration of robust XAI frameworks, alongside rigorous governance of generative models, is essential for ensuring equitable, safe, and clinically valid patient outcomes.
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