Chapter 17 - Healthcare using image recognition technology

Abstract

Machine learning approaches have revolutionized the domain of medical data analysis recently, enabling real-time processing and analysis of both text and image data. However, the important information obtained from electronic health records (EHR) and clinical notes has been extracted using image processing models such as support vector machine and machine learning methods. Therefore, these models have been employed for image processing projects like patient risk prediction, automatic diagnosis coding, and supporting medical decision-making. Moreover, the segmentation process has been performed in image processing to process medical pictures such as X-rays, magnetic resonance imaging scans, and computed tomography scans. These techniques have been utilized to help radiologists in their diagnostic work as well as to determine and categorize anomalies and diseases as well. Furthermore, real-time image processing has demonstrated efficient and more accurate diagnosis and treatment, leading to improved patient outcomes. Additionally, the recent studies in machine learning have also led to enhance the models that can incorporate both text and image data, leading for more comprehensive and extensive analysis of medical data. Therefore, these models have been performed for disease diagnosis and prediction, personalized treatment recommendation, and drug discovery. Ultimately, machine learning models have shown as powerful tools for real-time text and image processing in medical data analysis. With advance research and development, these models have the potential to revolutionize healthcare delivery and enhance patient outcomes. This chapter introduces some common image processing models, that is image segmentation, object detection, and image classification that are exploited in EHR data. Machine learning models have demonstrated valuable insight into EHR data, enabling healthcare providers to guarantee better decisions and improve patient outcomes.