Trust, Ethics, and User-Centric Design in AI-Integrated Genomics

Abstract

This study examines the integration of genomics and artificial intelligence (AI) in the healthcare industry, focusing on the ethical and trust-related issues that arise from this integration. This integration highlights the significance of protecting genomic data by employing homomorphic encryption. This study emphasizes the significance of algorithmic transparency. It suggests employing interpretative frameworks like Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to enhance the comprehensibility of AI algorithms. The article discusses two key regulatory measures: the Trustworthy Artificial Intelligence Initiative and the Genomic Data Sharing (GDS) Policy. This study examines the effectiveness of current practices in adapting to rapid technological advancements while maintaining ethical standards. This emphasizes the significance of attaining a balance between the benefits of predictive analytics in healthcare and the ethical considerations, such as informed consent and data integrity, as we transition from big data to big mechanisms. The importance of integration lies in its ability to revolutionize the healthcare sector. The study highlights the importance of robust governance frameworks to ensure technological advancements adhere to ethical standards and earn public trust. In summary, it is imperative to acknowledge and address the ethical considerations associated with integrating genomics and AI in healthcare to ensure effective and responsible implementation. This area is a major focus in contemporary medical research and practice.

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