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
Early and accurate classification of skin lesions is critical for effective skin cancer diagnosis and treatment. However, the visual similarity of lesions in their early stages often leads to misdiagnoses and delayed int…
The Internet of Medical Things (IoMT) is transforming healthcare through extensive automation, data collection, and real-time communication among interconnected devices. However, this rapid expansion introduces significa…
Plant disease detection is a crucial technology to ensure agricultural productivity and sustainability. However, traditional methods tend to fail as they do not address imprecise and uncertain data in a satisfactory way.…
Correct precision techniques have far not been introduced for modeling the modality risk in Intensive Care Unit (ICU) patients. Traditional mortality risk prediction techniques effectively extract the data in longitudina…
Alzheimer's Disease (AD) is a persistent, irreversible, and degenerative neurological disorder of the brain that currently has no effective therapy. This condition is identified by pathological abnormalities in the hippo…
In smart cities, the e-healthcare systems aided by Internet of Things (IoT) technologies play a significant role in proficient health monitoring services. The sensitivity and number of users in health networks highlights…
Cardiovascular disease is a critical threat to human health, as most death cases are due to heart disease. Although several doctors employ stethoscopes to auscultate heart sounds to detect abnormalities, the accuracy of…
Timely and accurate tumor detection in medical imaging is crucial for improving patient outcomes and reducing mortality rates. Traditional methods often rely on manual image interpretation, which is time-intensive and pr…
This research focuses on predicting Wisconsin Breast Cancer Disease using machine learning algorithm, employs a dataset offered by UCI repository (WBCD) dataset. The under- gone substantial preparation, includes managing…
Heart disease is one of the main heart diseases that cause the death of people worldwide, affecting the engine of the human body: the heart. It has a greater incidence in underdeveloped countries such as Angola, Banglade…
