A Comprehensive Literature Survey on AI-Driven Clinical Decision Support Systems for Automated Chest X-ray Analysis
Keywords:
Abnormality detection, Automated Chest X-ray (CXR), Computer-Aided Detection (CAD), Convolutional Neural Networks (CNNs), Deep learning, Heatmaps, Lung field segmentation, Pixel-level analysisAbstract
The Chest X-ray Clinical Decision Supported by Artificial Intelligence (AI) The Support System (CDSS) is designed to help healthcare professionals. to identify lung-related diseases with the help of chest X-ray images accurately and at an early stage. This project uses deep learning and machine learning to study X-ray images and distinguish the possible presence of irregularities. Algorithms, namely CNNs or convolutional neural networks. such conditions as respiratory diseases, tuberculosis, and pneumonia. The system takes medical images, obtains significant features, and delivers predictive outcomes to aid in clinical decision-making. The AI-based system eliminates human mistakes and minimizes the work of the hands. CDSS. and promotes timely treatment by accelerating the process of diagnosis. This project demonstrates that artificial intelligence can and will improve the analysis of medical imaging and be a supporting tool to doctors, rather than a replacement tool, and improve the overall effectiveness of healthcare and patient outcomes. Automated Chest X-ray (CXR) Analysis Decision Support Systems (DSS) are revolutionizing radiology by offering fast, accurate, and interpretable support to physicians in the detection of thoracic diseases. CNNs and Transformers are artificial eyes that help to eliminate reporting delays and human weariness, especially in high-throughput settings. These systems are often used in deep learning. Agent-Based Decision Frameworks are recent systems that employ intelligent agents to control a closed-loop process that integrates perception, memory, and reasoning to provide contextual decision assistance. Multimodal Learning & RAG systems are trending towards Retrieval-Augmented Generation (RAG) and cross-modal retrieval, directly matching photos to historical text reports for evidence-based, structured Electronic Medical Record (EMR) drafting.References
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