Smart Medicine: Exploring the Landscape of AI-Enhanced Clinical Decision Support Systems

¹ Department of Computer Science & Engineering, KG Reddy college of Engineering &Technology, Moinabad, Hyderabad, Telangana, India
² Department of Computer Science & Engineering, KG Reddy college of Engineering & Technology, Moinabad, Hyderabad, Telangana, India
³ Department of CSE, GRIET, Hyderabad, Telangana, India
⁴ Lovely Professional University, Phagwara, Punjab, India.

^* Corresponding author: srjhade@kgr.ac.in

Abstract

A Clinical Decision Support System (CDSS) combines medical knowledge with patient data to help healthcare providers make well-informed decisions. It offers real-time advice and recommendations for better patient outcomes and treatment management. CDSS enhances clinical decision-making by analysing information, identifying patterns, and offering evidence-based insights at the point of care. This abstract delves into the realm of Smart Medicine, investigating the application of AI-enhanced Clinical Decision Support Systems (CDSS) through the utilization of two prominent Convolutional Neural Network (CNN) architectures—VGGNet and ResNet. The study explores the landscape of these advanced systems in the healthcare domain, emphasizing the role of VGGNet's simplicity and transfer learning capabilities, and ResNet's innovative approach to addressing the challenges of training deep networks. The research scrutinizes their efficacy in capturing intricate medical patterns, offering insights into the nuanced decision-making processes within clinical settings. By navigating the landscape of AI-driven CDSS, this study contributes to the ongoing dialogue on optimizing healthcare outcomes through the integration of sophisticated neural network architectures. The findings shed light on the potential benefits and considerations associated with VGGNet and ResNet in shaping the future of AI-enhanced clinical decision support in Smart Medicine.