This white paper examines how artificial intelligence and machine learning can be responsibly deployed across patient analytics and clinical research. It defines “trustworthy AI” through five pillars: transparency, fairness, robustness, privacy, and accountability. The paper highlights real-world risks such as algorithmic bias, data leakage, lack of explainability, and regulatory misalignment, particularly in healthcare settings with sensitive patient data. It proposes governance frameworks, validation checkpoints, and human-in-the-loop oversight to ensure ethical deployment. Use cases include patient stratification, outcome prediction, and research optimization. The paper concludes that trustworthy AI is essential to sustaining regulatory confidence, clinician adoption, and long-term valu

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