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 October 2026
Anatomy education continues to rely on cadaver dissection, which is constrained by donor shortages, costs, and limited hands-on access. Digital 3D anatomy tools offer alternatives, but most do not collaborate well across…
Reliable Alzheimer’s disease (AD) magnetic resonance imaging (MRI) classification requires evaluation beyond accuracy alone. This study investigates a four-class image-level framework based on ImageNet-pretrained ResNet-…
Apple diseases cause great losses in fruit yield and quality, while manual diagnosis is time-consuming, subjective, and difficult to scale across orchards. Deep learning has thus become a staple in image-based disease re…
Cardiovascular disease remains a major public health burden and is associated with demographic, behavioral, and chronic-health characteristics. This study evaluates explainable machine learning for cross-sectional classi…
Diabetic Foot Ulcers (DFUs) are one of the most serious but preventable complications of diabetes mellitus that often develop without clinical signs until the late stages. This means that neuropathic and vascular abnorma…
Smart hospitals deploy Internet of Medical Things (IoMT) sensors and MQTT brokers to stream clinical telemetry over resource-constrained edge gateways. Centralized network intrusion detection systems (NIDS) expose sensit…
Based on Sommerville’s robust software reliability theory and eight design principles based on best practices (DPG), this study conducted a comparative assessment of two Dexcom software platforms: the Dexcom Clarity web…
Medical institutions increasingly hold data that is relational rather than tabular: patients linked by medical history, diagnostics by dependency, and doctors by consultations. Graph neural networks (GNNs) are a natural…
Drug-drug interactions (DDIs) are a leading cause of preventable adverse drug events, and the standard benchmark corpus for extracting them from biomedical text is severely im-balanced towards non-interacting pairs, maki…
Continuous and real-time health monitoring is essential for the early detection of cardiovascular and physiological abnormalities in remote and resource-limited settings. This research aims to develop an intelligent IoT-…
