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
The prediction of chronic kidney disease (CKD) must have models capable of processing heterogeneous clinical data and being transparent to assist clinical decision making. Current CKD research usually uses single-view da…
This is a clinically reliable and explainable diagnostic framework for the early detection of Alzheimer's disease with multimodal data. Current computational methods face challenges in dealing with fragmented clinical in…
You Only Look Once (YOLO) object detection network has gained significant adoption in the field of plant leaf disease detection due to its strong detection capabilities. However, deploying YOLO models on resource-constra…
Timely prediction of zoonotic disease outbreaks, particularly Highly Pathogenic Avian Influenza (HPAI), is critical for real-time epidemiological surveillance and pandemic pre-paredness. However, real-world avian surveil…
Large Language Models such as GPT-4o and GPT-4o-mini have shown significant promise in various fields. However, hallucination, when models generate inaccurate information, remains a critical challenge, especially in doma…
Medical imaging is one of the cornerstones of modern medicine, planning treatments, monitoring patient progress and aiding clinicians in diagnosing diseases such as tumors, cancer, and many others. With the rise of neura…
As artificial intelligence (AI) advances in healthcare, its use in maternal health shows promise but faces challenges of trust due to the black-box nature of many models. Gestational diabetes mellitus (GDM), a transient…
Face recognition systems have become prevalent in mobile devices and security applications, increasing the demand for robust face presentation attack detection. Early efforts based on handcrafted features struggled to co…
Breast cancer is one of the most common and deadly diseases affecting women around the worldwide. It is specially affecting in regions where has limited access to advanced diagnostic tools. Recent studies have shown that…
Technological advances in automated medical imaging diagnosis have created translation gaps between laboratory achievements and clinical implementation, with traditional manual Cobb angle measurement requiring considerab…
