MACHINE LEARNING FOR RAPID DIAGNOSIS IN EMERGENCY HEALTHCARE

Abstract

The rapid and precise identification of severe circumstances in emergency healthcare remains a substantial challenge due to time constraints, increased patient numbers, and restricted resources. Recent advances in machine learning (ML) have led to the creation of groundbreaking technologies that help doctors make better decisions, speed up the process of getting a diagnosis, and improve patient outcomes. This paper gathers current uses of machine learning in emergency care, such as using AI to improve telemedicine platforms, wearable-based real-time monitoring, and natural language processing of clinical data. Numerous studies indicate that machine learning models outperform conventional scoring systems in predicting sepsis, expedite the interpretation of CT and X-ray scans and enhance triage processes to optimize resource utilization. Integrating machine learning (ML) provides many other benefits besides boosting clinical accuracy. For example, it lowers the chance of human error, speeds up operations and makes it possible to diagnose patients in remote and low-resource places. But there are still concerns, especially with the quality of the data, the bias of the model, the ethical obligation and how well it fits with clinical practices. This paper highlights ML's potential to transform emergency care by analyzing diagnostic methods, advantages and constraints, while stressing the necessity for clear, understandable, and fair solutions.