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
With the advent of new sensor device designs, IoT based medical applications are increasingly being employed. This study introduces BlockFaaS: a Blockchain-assisted serverless framework that incorporates advanced AI mode…
Parkinson's disease (PD) is a progressive and chronic neurodegenerative disorder characterized by motor impairment, speech deficits, and cognitive decline. Monitoring disease progression accurately and intermittently is…
Semantic segmentation is an important operation in computer vision, which is generally plagued by computational resources and the time-consuming process for labor intensive of pixel-wise labeling. As a solution to this i…
Malaria persists as a serious global public health threat, particularly in resource-limited regions where timely and accurate diagnosis is a challenge due to poor medical infrastructure. This study presents a comparative…
Alzheimer’s Disease (AD) is a terminal illness affecting the human brain that leads to deterioration of cognitive function and should therefore be diagnosed as early as possible. The goal of this work is to come up with…
Background: Ocular Toxoplasmosis, a leading cause of Posterior Uveitis, demands timely diagnosis to prevent vision loss. Manual retinal image analysis is labor-intensive and variable, while existing Deep Learning models…
The Internet of Medical Things (IoMT) refers to smart devices that are used in their transformation of the healthcare sector with continuous monitoring in real time, remote diagnostics as well as real time data exchange.…
Alzheimer’s disease (AD) diagnosis using MRI is hindered by data-sharing restrictions. This study investigates whether federated learning (FL) can achieve high diagnostic accuracy while preserving data confidentiality. W…
Electronic Medical Record (EMR) is a commonly used tool in medical diagnosis, which has static recording, difficulty in combining and analyzing different forms of data, and insufficient diagnostic efficiency and accuracy…
Alzheimer’s disease (AD) is a leading cause of dementia, yet its diagnosis remains challenging. EEG provides a noninvasive and cost-effective method for monitoring brain activity, which may reflect both cognitive decline…
