GenAI Integration in Clinical Decision Support Systems: Towards Responsible and Scalable AI in Healthcare

Abstract

Generative Artificial Intelligence (GenAI) holds transformative potential for Clinical Decision Support Systems (CDSS), enabling automated, data-driven insights across diagnostic and therapeutic workflows. This paper explores the technical and ethical dimensions of integrating GenAI into CDSS, with a focus on medical imaging for brain tumor segmentation. We propose a hybrid architecture combining U-Net with transformer-based attention mechanisms to enhance both local feature extraction and global contextual understanding. Using the BraTS 2020 dataset, which comprises expert-labeled multi-modal MRI scans, the model demonstrates significant performance gains— achieving Dice Similarity Coefficients of 92.4% for Whole Tumor, 88.3% for Tumor Core, and 85.7% for Enhancing Tumor. These metrics outperform standard U-Net and Attention U-Net architectures by notable margins. Beyond technical accuracy, we examine the implications of deploying such systems in real-world healthcare settings, including considerations of algorithmic bias, transparency, data governance, and clinical accountability. This work contributes to the dialogue on responsible GenAI deployment, proposing an interpretable and scalable AI framework aligned with clinical needs and ethical standards. By addressing both performance and policy, the paper advocates inclusive, safe, and socially beneficial applications of GenAI in healthcare.

Ähnliche Arbeiten