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
This work proposes a computational framework for modeling semantic relationships between medical specialties using large language models. Forty-four medical specialties officially recognized in France were analyzed using…
Early detection of skin pathology is critical for patient survival and treatment effectiveness, particularly in the case of aggressive malignancies such as melanoma. In this study, we propose a lightweight and explainabl…
Phenotyping inflammatory bowel disease (IBD) from gut microbiome profiles remains challenging due to 93% genus-level zero-inflation, skewed amplicon count distributions, and the practical cost of assembling labelled coho…
The protection of medical images in healthcare services has become important due to the adoption of telemedicine and cloud-based healthcare services. Conventional watermarking methods embed information directly into the…
Accurate classification of heart sounds is critical for the early detection and diagnosis of cardiovascular diseases. This research presents an automated technique for classifying heart sounds into normal, murmur, and ex…
Upon arrival at a hospital, patients require an initial assessment to determine the urgency of their condition and the appropriate medical specialty for their needs. This manual triage process, however, is often time-con…
The rapid integration of artificial intelligence (AI) into healthcare systems has intensified the need for governance frameworks that ensure safety, accountability, ethical, and sustainable deployment. However, existing…
Ensuring strong security readiness in telemedicine is essential to protect patients, safeguard their data, and build confidence and satisfaction in today’s digital healthcare platforms. Hence, this research explores and…
To address the challenges of feature extraction in complex field environments, the limited sensitivity of YOLOv9 to subtle disease features, and the lack of adaptive hyperparameter optimization, this paper proposes an im…
Brain image registration is fundamental for medical imaging to allow the matching of images from multiple modalities, temporal sequences, and people to offer spatial correlation. This is crucial for activities such as co…
