The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.
Artificial intelligence and machine learning in healthcare apply computational methods to clinical and biomedical data to support diagnosis, treatment planning, and health system operations. Common applications include medical image analysis using convolutional neural networks to detect tumors, fractures, and other abnormalities in radiology and pathology images; predictive models that estimate patient risk for readmission, sepsis, or disease progression from electronic health records; natural language processing for extracting information from clinical notes; and drug discovery models that predict molecular properties and candidate compounds. Regulatory activity reflects this growth: the FDA had authorized more than 1,300 AI-enabled medical devices by December 2025, with a record 295 cleared that year alone. Deploying AI in clinical settings raises distinct requirements around model interpretability for clinician trust, validation across diverse patient populations, and integration with existing hospital information systems. As an open-access healthcare AI journal, IJACSA publishes research on AI and machine learning models in healthcare, their clinical validation, and applied systems for diagnostic support and patient monitoring.
Published in International Journal of Advanced Computer Science and Applications (IJACSA) · list last refreshed September 2026
This study explores the implementation of a Dose Archiving and Communication System (DACS) in Moroccan healthcare, highlighting the importance of X-ray dose management in modern radiology. It emphasizes patient safety an…
With technology innovations such as Artificial Intelligence (AI) and Internet of Things (IoT), unprecedented applications and solutions to real world problems are made possible. Precision agriculture is one such problem…
Multimodal medical image fusion leverages the correlation between different modal images to enhance the information contained within a single medical image. Existing fusion methods often fail to effectively extract multi…
This study explores the application of the VGG19 convolutional neural network (CNN) model, pre-trained on ImageNet, for the classification of rice crop diseases using image segmentation techniques. The research aims to e…
Chest X-ray imaging is the main tool for detecting tuberculosis (TB), providing essential information about pulmonary abnormalities that may indicate the presence of the disease. Still, manual interpretation is a common…
Cacao, scientifically known as Theobroma cacao, is a highly nutritious food and is extensively utilized in multiple sectors, including agriculture and health. Nevertheless, the agricultural sector encounters notable obst…
The potential of Internet-of-Things (IoT) in healthcare is evident in its ability to connect medical equipment, sensors, and healthcare personnel to provide high-quality medical expertise in remote locations. The constra…
The application of machine learning, particularly through image-based analysis using computer vision techniques, has greatly improved the management of crop diseases in agriculture. This study explores the use of transfe…
Efficient secured storage and retrieval of medical surgical videos are essential for modern healthcare systems. Traditional methods often struggle with scalability, accessibility, and data security, necessitating innovat…
Ever since its outbreak, numerous research studies have been initiated worldwide as an attempt for an accurate and efficient diagnosis of COVID-19. In the recent past, patients suffering from various chronic lung disease…
