MedicalArc

The healthcare industry faces significant challenges in efficient resource management and timely disease diagnosis. Traditional hospital management systems often lack intelligent automation, leading to inefficiencies such as high appointment no-show rates, delayed diagnosis of critical conditions, and suboptimal resource utilization. This project presents an AI-powered Hospital Management System that addresses these challenges through the integration of machine learning and deep learning technologies. The system comprises two primary modules: an Intelligent Appointment Management System and an AI-based Chest XRay Analysis System for lung disease detection. The appointment module leverages machine learning algorithms to predict patient no-show probability, optimize scheduling through intelligent time-slot recommendations, and provide conversational appointment booking via an AI chatbot. This reduces waiting times, improves resource allocation, and enhances patient experience. The X-Ray analysis module employs deep learning techniques, specifically Convolutional Neural Networks (CNNs) with transfer learning approaches using architectures such as DenseNet121 and ResNet50. The system can detect multiple lung conditions including pneumonia, tuberculosis, COVID-19, pleural effusion, cardiomegaly, and other pulmonary abnormalities with high accuracy. It generates automated preliminary reports with confidence scores, provides visual explanations This integrated approach demonstrates the potential of artificial intelligence in transforming healthcare delivery by reducing diagnostic delays, minimizing human error, optimizing hospital operations, and ultimately improving patient outcomes. The system serves as a proof-of-concept for how modern AI technologies can be effectively deployed in realworld clinical settings while maintaining explainability and reliability standards required in medical applications.