Abstract

The integration of artificial intelligence (AI) in healthcare has transformed traditional diagnostic processes, offering enhanced speed, accuracy, and scalability. This study explores the application of deep learning algorithms in the early detection of chronic and acute diseases, focusing on their implementation within clinical decision-support systems (CDSS). Specifically, we evaluate a convolutional neural network (CNN) architecture trained on a diverse dataset of medical imaging and electronic health records (EHRs) related to cardiovascular diseases, diabetes, and pulmonary conditions.The model demonstrated superior performance compared to traditional diagnostic tools, achieving an average accuracy of 94.6% and a precision-recall balance that reduced false positives by 28%. Notably, the system was capable of identifying subtle patterns in imaging data that were often overlooked by human practitioners. This early detection capability is crucial in preventing disease progression and reducing healthcare costs associated with late-stage interventions.The study also highlights the importance of data preprocessing, including normalization and augmentation techniques, in optimizing model performance. Ethical considerations, such as data privacy and algorithmic transparency, were addressed by employing federated learning methods and explainable AI frameworks.Our findings suggest that integrating AI-powered diagnostic systems into clinical workflows can significantly improve patient outcomes, particularly in under-resourced healthcare settings where access to specialist care is limited. The paper concludes by recommending best practices for deploying AI in real-world healthcare environments, emphasizing the need for cross-disciplinary collaboration, rigorous validation, and adherence to regulatory standards.This research contributes to the growing body of evidence supporting AI as a transformative tool in medicine and paves the way for future innovations in predictive healthcare technologies.The integration of artificial intelligence (AI) in healthcare has transformed traditional diagnostic processes, offering enhanced speed, accuracy, and scalability. This study explores the application of deep learning algorithms in the early detection of chronic and acute diseases, focusing on their implementation within clinical decision-support systems (CDSS). Specifically, we evaluate a convolutional neural network (CNN) architecture trained on a diverse dataset of medical imaging and electronic health records (EHRs) related to cardiovascular diseases, diabetes, and pulmonary conditions.The model demonstrated superior performance compared to traditional diagnostic tools, achieving an average accuracy of 94.6% and a precision-recall balance that reduced false positives by 28%. Notably, the system was capable of identifying subtle patterns in imaging data that were often overlooked by human practitioners. This early detection capability is crucial in preventing disease progression and reducing healthcare costs associated with late-stage interventions.The study also highlights the importance of data preprocessing, including normalization and augmentation techniques, in optimizing model performance. Ethical considerations, such as data privacy and algorithmic transparency, were addressed by employing federated learning methods and explainable AI frameworks.Our findings suggest that integrating AI-powered diagnostic systems into clinical workflows can significantly improve patient outcomes, particularly in under-resourced healthcare settings where access to specialist care is limited. The paper concludes by recommending best practices for deploying AI in real-world healthcare environments, emphasizing the need for cross-disciplinary collaboration, rigorous validation, and adherence to regulatory standards.This research contributes to the growing body of evidence supporting AI as a transformative tool in medicine and paves the way for future innovations in predictive healthcare technologies.