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
Chest diseases significantly affect public health, causing more than one million hospital admissions and approximately 50,000 deaths annually in the United States. Chest X-ray imaging technology, which is a critically im…
Differentiation of Alzheimer's Disease (AD) and Dementia with Lewy Bodies (DLB) utilizing brain perfusion Single Photon Emission Tomography (SPECT) is crucial and it might be difficult to distinguish between the two illn…
The monkeypox epidemic has spread to nearly every nation. Governments implement several strict policies, to stop the virus that causes monkeypox. For effective handling and treatment, early identification and diagnosis o…
Despite advancements in machine learning within healthcare, the majority of predictive models for ICU mortality lack interpretability, a crucial factor for clinical application. The complexity inherent in high-dimensiona…
Early detection and precise prediction are essential in medical diagnosis, particularly for diseases such as diabetic nephropathy (DN), which tends to go undiagnosed at its early stages. Conventional diagnostic technique…
Medical records are essential for disease detection to help establish a diagnosis. Many issues with imbalanced classification are discovered in many cases of early disease detection and diagnosis using machine learning m…
Noninvasive and accurate methods for diagnosing respiratory diseases are essential to improving healthcare consequences. The Internet of Medical Things (IoMT) is critical in driving developments in this field. This work…
Cardiac disease classification is a crucial task in healthcare aimed at early diagnosis and prevention of cardiovascular complications. Traditional methods such as machine learning models often face challenges in handlin…
Chronic Kidney Disease (CKD) is a chronic disease that progressively impairs kidney function to the point of wasting filtration, electrolyte imbalance, and blood pressure control. Early and precise prediction becomes nec…
Seizures are a serious neurological disease, and proper prognosis by electroencephalography (EEG) dramatically enhances patient outcomes. Current seizure prediction methods fail to deal with big data and usually need int…
