AI's incorporation into clinical trials represents a paradigm shift rather than merely an invention. It's about influencing the course of healthcare by improving patient and healthcare provider access to, personalization of, and transparency in clinical trials. We can speed the development of life-saving therapies, promote transparent and patient-centric trial environments, and transform clinical trial outcomes by utilizing AI. Artificial intelligence technology are integrated into numerous parts of healthcare delivery to improve patient outcomes, efficiency, and accuracy.
Published in Chapter:
Detection of Heart Disease Using ANN: Present Research and Future Opportunities
Copyright: © 2024
|Pages: 15
DOI: 10.4018/979-8-3693-3629-8.ch009
Abstract
Heart disease remains one of the leading causes of mortality worldwide. Early detection and accurate diagnosis are crucial for effective treatment and prevention of cardiac complications. Artificial neural networks (ANNs) have emerged as powerful tools for heart disease detection, leveraging their ability to learn complex patterns from data. This chapter comprehensively reviews recent studies and developments in the application of ANNs for heart disease detection, highlighting their strengths, challenges, and future directions. The chapter also explores opportunities for the field, imagining the use of federated learning for collaborative model development, the integration of AI-driven decision support systems into standard clinical workflows, and the use of explainable AI techniques to improve model interpretability. It investigates a number of methods, such as the integration of multimodal data sources, convolutional neural networks (CNNs) for image-based diagnosis, risk prediction models, and ECG analysis.