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
Efficient and accurate automated diagnosis of plant diseases remains a challenge for deployment on resource-constrained edge devices. While hybrid vision transformers like GCViT balance accuracy and efficiency, they ofte…
The Researchers and academicians are continuously working on minimizing the production losses due to various plant diseases. Therefore, recent technologies such as artificial intelligence (AI), and machine learning (ML)…
Traditional single-architecture neural models, in-cluding monolithic transformer-based and sequence-to-sequence architectures, often struggle to extract Adverse Drug Reactions (ADRs) from patient-generated health narrati…
The rapid development of medical practices and imaging technology tools creates substantial growth in the amount of medical image data each year in our present era. This research aims to develop a hybrid approach that in…
This study presents a comprehensive structural and mathematical security analysis of LightAuth, a lightweight authentication framework, specifically designed for smart health sensor networks. We delve into its core compo…
Radiology reports encode critical clinical observations from medical imaging in an unstructured textual form that is central to modern clinical diagnosis and decision support. In this context, natural language processing…
Cardiovascular disease is still the leading cause of death, and a definitive cure has not yet been found, so this is the time to make important changes in prevention and early diagnosis. Integrating artificial intelligen…
Crohn's Disease (CD) is a long-term inflammatory bowel disorder that affects the digestive system. It is influenced by geography, diet, genetics, and immune response. Patients often experience difficulties managing CD du…
Segmentation of the liver in ultrasound images is a critical task in medical image analysis, yet it remains challenging due to acoustic speckle noise, brightness instability, and deformations caused by probe pressure. To…
Plant diseases pose a serious threat to agricultural productivity, which can cause significant crop losses if not addressed quickly and appropriately. There are significant opportunities for digital image-based treatment…
