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
Heart disease remains a global health concern, demanding early and accurate prediction for improved patient outcomes. Machine learning offers promising tools, but existing methods face accuracy, class imbalance, and over…
Glaucoma and cataracts are leading causes of blindness worldwide, resulting in significant vision loss and quality of life impairment. Early detection and diagnosis are crucial for effective treatment and prevention of f…
Data classification, a crucial practice in information management, involves categorizing data based on its sensitivity to determine appropriate access levels and protection measures. This paper explores the utilization o…
Alzheimer's disease (AD) poses a significant challenge to modern healthcare, as effective treatment remains elusive. Drugs may slow down the progress of the disease, but there is currently no cure for it. Early AD identi…
It is important to note that the cure rates in cases of advanced stages of lung cancer are remarkably low, which stresses out the importance for early detection as means to increase survival chances. A strong area of foc…
In the past decade, Electronic Health Records (EHRs) based on clouds have become popular in empowering remote patient monitoring. The rise of Health 4.0, which includes using system elements and cloud services to access…
Aiming at addressing the challenges of large model parameters, high computational cost, and low accuracy of the traditional tea disease identification model, an improved MobileNet model integrated spatial and channel att…
In modern medical diagnostics, Deep Learning models are commonly used for illness diagnosis, especially over X-ray chest images. Deep Learning approaches provide unmatched promise for early identification, prognosis, and…
In the expanding field of medical imaging, precise segmentation of anatomical structures is critical for accurate diagnosis and therapeutic interventions. This research paper introduces an innovative approach, building u…
Biomedical condition monitoring devices are progressing quickly by incorporating cost-effective and non-invasive sensors to track vital signs, record medical circumstances, and deliver meaningful responses. These sophist…
