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
Regulated AI creates a monitoring problem for deployers who must organise human oversight, log-retention and post-market surveillance while often having access only to the prediction interface. This study specifies a dep…
The diagnosis of plant diseases in Capsicum species remains a critical challenge in precision agriculture due to variability in environmental conditions and limited availability of high-quality datasets. The traditional…
In modern healthcare, medical imaging has a significant role in understanding the structure and functioning of the human body, which helps doctors to diagnose, to plan the treatment, and to monitor the disease. Chest X-r…
White Spot Disease (WSD) is a major threat to aquaculture production and requires accurate image-based detection methods for early diagnosis. However, disease marker detection in fish images is challenging due to noise,…
Alzheimer's disease (AD) refers to a progressive neurodegenerative disease involving cognitive impairment, brain atrophy, and functional neurological deficits that make early prediction and subsequent disease progression…
Alzheimer’s disease (AD) is associated with widespread disruption of functional brain networks. While graph-theoretic studies have characterized altered connectivity patterns in Alzheimer’s disease, most of the analyses…
Chronic Kidney Disease (CKD) is a progressive and irreversible condition that requires early prediction of renal deterioration for effective clinical intervention. Existing studies based on static machine learning and co…
Renal pathology represents a diverse set of diseases that present significant clinical relevance. Included among the various types of renal pathologies are renal stones, cysts, and renal malignancies, all of which requir…
Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide, where early and accurate diagnosis plays a vital role in improving survival rates. Recent advancements in deep learning h…
Protecting patient data confidentiality while enabling collaborative machine learning across distributed healthcare institutions remains a major challenge. This study presents ZK-FedMed, a privacy-preserving federated le…
