Trustworthy and Efficient Generative AI Systems using Google Vertex AI: A Framework for Ethical Data-Driven Automation in Healthcare

<p><strong>Abstract:</strong><br>This paper presents a reproducible framework for building <em>Trustworthy and Efficient Generative AI Systems</em> using <strong>Google Vertex AI</strong>. The proposed architecture, called the <strong>Trust-Augmented Generative Framework (TAG-AI)</strong>, integrates explainability, bias auditing, and human-in-the-loop supervision to ensure responsible and ethical AI automation—particularly in healthcare applications. Using open datasets such as <strong>MIMIC-III Clinical Database</strong> and <strong>UCI Heart Disease</strong>, the framework demonstrates improved interpretability, reduced bias, and optimized latency without compromising performance.</p> <p>The TAG-AI architecture aligns with global research efforts in <strong>Responsible AI</strong>, <strong>MLOps Governance</strong>, and <strong>Ethical Data Science</strong>, operationalizing theoretical AI ethics into deployable, measurable pipelines. The study highlights how cloud-native platforms like <strong>Google Vertex AI</strong> can bridge academic research and industrial AI implementation through continuous trust scoring, bias detection, and explainability-driven monitoring.</p> <p><strong>Keywords:</strong> Generative AI, Vertex AI, Trustworthy AI, Explainability, MLOps, Responsible AI, Healthcare Analytics, Ethical Automation.</p> <p><strong>Author:</strong> Mohammed Abdul Rahman Khan<br><strong>Affiliation:</strong> Muffakham Jah College of Engineering and Technology, Osmania University, India.</p> <p>This research contributes to the growing field of Responsible AI and aligns with the ethical AI vision promoted by institutions such as MBZUAI and IEEE.</p>