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
The Internet of Things (IoT) has emerged as a trend in the healthcare industry to develop innovative solutions that enhance patient outcomes and operational efficiency. Healthcare has become more accessible, affordable,…
Watermarking schemes ensure digital image security and copyright protection to prevent unauthorized distribution. Zero-watermarking methods do not modify the image. This characteristic is a requirement in some tasks that…
The enormous amounts of data produced in the healthcare sector are managed and analyzed with the help of Apache Spark, an open-source distributed computing system. This case study examines how Spark is utilized in the he…
In recent years, fused images have been developed for fast processing of medical images, which provide a more reliable basis for reducing the burden on physicians because they can contain multiple times the image informa…
In recent years, Machine Learning (ML) techniques have been used by several researchers to classify diseases using gene expression data. Disease categorization using heterogeneous gene expression data is often used for d…
This study presents a comparative analysis of different deep convolutional neural network (DCNN) architectures, including VGG19, NASNet, ResNet50, and MobileNetV2, with and without data augmentation, for the automatic de…
Agent technology has provided many opportunities to improve the human standard of life in recent decades, starting from social life and moving on to business intelligence and tackling complicated communication, integrati…
Image segmentation is considered one of the essential tasks for extracting useful information from an image. Given the brain tumor and its consumption of medical resources, the development of a deep learning method for M…
Timely disease diagnosis in paddy is fundamental to preventing yield losses and ensuring an adequate supply of rice for a rapidly rising worldwide population. Recent advancements in deep learning have helped overcome the…
Preventing and controlling grape diseases is essential for a good grape harvest. With the help of “single shot multi-box detectors”, “faster region based convolutional neural networks”, & “You only look once-X,” the stud…
