Deployment of AI Models and a Mapper Algorithm to Enhance Clinical Decision-Making

This paper highlights the opportunities that foundational models and localised model development present to tackle real world challenges through deployment of these models into production and use. We present an AI-driven Clinical Decision Support System (CDSS) using the R Shiny framework, tailored for frontline health workers. Building on work that demonstrated good performance of hybrid AI models with high accuracy for detecting COVID-19 using chest X-ray data, this study demonstrates how these models can be integrated into a practical, scalable CDSS. The system enables health workers to upload X-ray images, receive AI-generated diagnoses, and access a Multimodal GPT-generated radiology report. Additionally, the CDSS includes a customized GPT Assistant based on local clinical guidelines and an epidemiological alert feature using Topological Data Analysis (TDA) for real-time monitoring. The findings suggest that the integration of such advanced tools into clinical workflows have potential to significantly enhance decision-making processes, particularly in resource-limited settings.