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 seeks to enhance the security, efficiency, and usability of Thailand’s health information system through the integration of blockchain technology and a user-friendly web application. Blockchain’s inherent stre…
Gait disorders in older adults, particularly those associated with neurodegenerative diseases such as Parkinson’s Disease, Huntington’s Disease, and Amyotrophic Lateral Sclerosis , present significant diagnostic challeng…
Healthcare informatics has revolutionized data extraction from large datasets. However, using analytics while protecting sensitive healthcare data is a major challenge. A novel methodology for Privacy-Preserving Analytic…
This study presents a novel approach for non-contact extraction of physiological parameters, such as heart rate and respiratory rate, from facial images captured using RGB cameras, leveraging recent advancements in deep…
Lack of diseases detection in plants frequently results in the spread of diseases that are difficult to treat and expensive. Rapid diseases recognition enables farmers to control the diseases with appropriate treatment.…
Leaf diseases pose a significant challenge to rice productivity, which is critical as rice is a staple food for over half of the world's population and a major agricultural commodity. These diseases can lead to severe ec…
In the healthcare sector, early and accurate disease detection is essential for providing appropriate care on time. This is especially crucial in thyroid problems, which can be difficult to diagnose because of their many…
This paper presents three significant contributions to the field of privacy-preserving Content-Based Image Retrieval (CBIR) systems for medical imaging. First, we introduce a novel framework that integrates VGG-16 Convol…
X-ray dosimetry practices are guided by international standards and regulatory agencies to ensure the safety of patients, radiation workers, and the general public. This paper introduces the Smart X-ray Geiger Data Logge…
Early and accurate detection of skin cancer is critical for effective treatment. This research aims to enhance skin cancer multi-class classification using transfer learning and Vision Transformers (ViTs), addressing the…
