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
Glaucoma is a common eye condition that can cause irreversible blindness if left untreated. Glaucoma can be identified by the optic nerve disorder (a perilous path that carries the potential risk) and leads to blindness.…
Climate change is one of the most talked-about topics of this decade, affecting all economic output sectors, including the economy of cow farming. In many scenarios, exceptionally severe climate change is predicted for t…
Chronic kidney disease (CKD) is a serious chronic illness without a definitive cure. According to WHO in 2015, 10% of the population suffers from CKD, with 1.5 million patients undergoing global haemodialysis. The incide…
The increasing use of electronic health records (EHRs) has led to a surge in unstructured data, making it challenging to extract valuable insights. This study proposes Natural Language Processing (NLP) based techniques t…
Cardiovascular disease (CVD) remains one of the leading causes of mortality worldwide, highlighting the need for early and precise prediction to support timely intervention. This study introduces an ensemble-based adapti…
During the Hajj season, Saudi Arabia experiences the arrival of millions of pilgrims from diverse linguistic and geographical backgrounds. This influx poses significant challenges for emergency medical care services. The…
Detecting wheat diseases and pests, particularly those characterized by small targets amidst complex background interference, presents a significant challenge in agricultural re-search. To address this issue and achieve…
Imbalanced datasets pose significant challenges in healthcare for developing accurate predictive models in medical diagnostics. In this work, we explore the effectiveness of combining resampling methods with machine lear…
In order to minimize the stochasticity of agents used in disease diagnosis within the dialogue system, and to enable them to interact with users based on the inherent connections between symptoms and diseases, while simu…
Blockchain is a peer-to-peer (P2P) network that distributes information and protects data integrity, security, and privacy. Constant simplification is required for information exchange. This comprehensive assessment seam…
