AI-DrivenX-Ray Analysis for Early Detection of Respiratory Conditions in Crisis Situations

Abstract

The increasing frequency and severity of global health crises, such as pandemics and mass casualty incidents, demand rapid and accurate diagnostic solutions. AI-driven X-ray analysis has emerged as a transformative technology for the early detection of respiratory conditions, providing high-speed and reliable diagnostics. This review examines the critical role of AI in enhancing the capabilities of X-ray imaging for conditions like pneumonia, tuberculosis, and COVID-19 in crisis scenarios. By improving diagnostic accuracy and scalability, AI supports timely decision-making and efficient resource allocation. The review also explores the technology's benefits, challenges, and future prospects, including ethical and regulatory considerations necessary for its successful deployment.
Aim of work
The aim of this review is to explore the transformative potential of AI-driven X-ray analysis for the early detection of respiratory conditions, particularly in crisis situations such as pandemics or health emergencies. Respiratory diseases, including pneumonia, tuberculosis, and COVID-19, present a significant global health burden, especially when healthcare systems are overwhelmed. Rapid and accurate diagnostics during such crises are critical for controlling disease spread, optimizing patient outcomes, and managing limited resources effectively.