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 study evaluates a three-factor authentication protocol designed for IoT healthcare systems, identifying several key vulnerabilities that could compromise its security. The analysis reveals weaknesses in single-facto…
Evaluating physicians' performance is one of the fundamental pillars of improving the quality of healthcare in medical institutions, as it contributes to measuring their ability to provide appropriate treatment, interact…
Automated detection of intestinal parasites in medical imaging enhances diagnostic efficiency and reduces human error. This study evaluates object detection techniques using Faster R-CNN with different backbone architect…
This paper introduces a novel framework integrating Large Language Models (LLMs) with blockchain technology for medical device fault detection and diagnostics in Health-care 4.0 environments. The proposed framework addre…
Image segmentation is an important aspect of image processing and analysis. Medical imaging segmentation is critical for providing noninvasive information about human body structure that helps physicians analyze body ana…
The evolution of healthcare, driven by remote monitoring and connected devices, is transforming medical service de-livery. Digital twins, virtual replicas of patients, enable continuous monitoring and predictive analysis…
Timely and precise identification of potato leaf diseases plays a critical role in improving crop productivity and reducing the impact of plant pathogens. Conventional detection techniques are often labor-intensive, depe…
Instance segmentation is a critical component of medical image analysis, enabling tasks such as tissue and organ delineation, and disease detection. This paper provides a detailed comparative analysis of two fine-tuned o…
Visual changes, including spots, discoloration, and deformation characterize coffee leaf diseases. In real-world image data, complex backgrounds present challenges for classification using deep learning models. Irrelevan…
As the continuous advancement of medical technology, image fusion technology has also been used in it. However, current medical image fusion systems still have drawbacks such as low image clarity, low accuracy, and slow…
