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 work presents a Model-Augmented Masked Autoencoder (MAE) framework, augmented with domain-adaptive denoising and multi-level feature fusion, for self-supervised representation learning in MRI-based brain tumor analy…
Lung diseases are detected effectively by Chest CT Medical im-ages. Chest CT images are 3D, and they are used for detecting abnormalities in the lungs like pneumonia, tuberculosis, tumors, etc. Chest CT images suffer fro…
Automated epileptic seizure detection from electroencephalogram (EEG) recordings remains a challenging biomedical signal analysis task because of the nonlinear, non-stationary, and high-dimensional characteristics of neu…
Heart disease remains one of the leading causes of mortality worldwide, motivating the development of accurate and scalable screening-support tools. This study presents an integrated framework for heart-disease status cl…
This research examines the challenges of AI adoption in Saudi Arabia healthcare sector in terms of the framework defined by the Vision 2030 healthcare transformation agenda. We utilized the Analytic Hierarchy Process (AH…
Pneumoconiosis remains a major occupational lung disease among workers exposed to silica, coal, and other in-organic dusts. Although chest X-ray screening is widely used in clinical practice, diagnostic performance is of…
With the widespread adoption of the Internet of Medical Things (IoMT), hospitals have become prime targets for cyberattacks. To overcome the limitations of traditional defenses and computationally heavy deep learning mod…
Foliar pathogens destroy roughly a third to two-fifths of harvestable crops each year, yet the deep convolutional classifiers that have proven most effective at automated leaf-image diagnosis carry parameter counts in th…
Subtyping inflammatory bowel disease (IBD) from gut microbiome sequencing data is a clinically demanding problem. Cross-cohort variability is large, and models trained on one dataset often fail on another. In this work,…
The increasing adoption of large language mod-els (LLMs) and domain-adapted transformers in healthcare has created a new privacy challenge: fine-tuned models may memorize rare clinical strings and later reveal them throu…
