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 October 2026
Healthcare systems face increasing pressure from nursing shortages, growing patient-care demands, and the substantial amount of time healthcare professionals spend performing routine and repetitive bedside activities. Th…
Cardiovascular disease (CVD) is the leading global cause of morbidity and mortality, with onset driven by a complex interplay between genetic susceptibility, demographic characteristics, and modifiable lifestyle factors.…
Rice is a vital crop for global food security, but its productivity is frequently threatened by diseases and pests, necessitating rapid and accurate detection. This study presents a semi-supervised deep learning approach…
Missing physiological samples create preprocessing uncertainty and uneven client reliability in federated learning. This study proposes Cross-Missingness Stable Trust federated learning (CMST-FL), a two-stage framework t…
Reliable apple leaf disease classification requires high predictive performance and transparent computational reporting under heterogeneous field conditions. This study presents an orchard-image benchmark of four ImageNe…
The healthcare industry is rapidly evolving with the digitalisation of medical devices. Despite the advantages of IoMT devices, they remain vulnerable to cyberattacks that threaten patient data security. Authentication t…
Federated learning is an emerging paradigm for collaborative model training in healthcare applications where sensitive patient data cannot be centralized due to privacy concerns; however, missing values in physiological…
Federated learning (FL) enables distributed intrusion detection without centralizing raw Internet of Things (IoT), Industrial IoT (IIoT), or Internet of Medical Things (IoMT) traffic. Secure aggregation protects update c…
Glioblastoma Multiforme (GBM) is the most invasive form of primary brain tumor. The average survival time after diagnosis is only 15 months. Accurately and automatically analyzing GBM MRI scans remains a major clinical c…
Remote patient monitoring systems based on the Internet of Medical Things (IoMT) increasingly use large language models (LLMs) to convert vital signs into readable alerts, but lightweight edge-deployed LLMs may hallucina…
