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 paper proposes an innovative ensemble learning framework for classifying medical images using Support Vector Machine (SVM) and Fuzzy Logic classifiers. The proposed approach utilizes logical AND and OR operations to…
Wireless Capsule Endoscopy (WCE) is a diagnostic technology for gastrointestinal tract pathology detection. It has emerged as an alternative to conventional endoscopy which could be distressing to the patient. However, t…
The burgeoning realm of digital healthcare has unveiled a novel diagnostic instrument: a digital stethoscope tailored for the early detection of heart disease as elucidated in this research. By harnessing the nuanced cap…
Smart healthcare is based on the electronic health and medical histories of residents, combined with information technology (IT) which can be used to construct a variety of systems including humanised health management s…
The rapid growth of application development has made applications an integral part of people's lives, offering solutions to societal problems. Health service applications have gained popularity due to their convenience i…
Medical image denoising plays an important role for the noise in the medical images can reduce the visibility, thereby affecting the diagnostic results of the doctors. Although good results have been achieved by the well…
Prostate cancer is one of the leading causes of cancer-related deaths among men. Early detection of prostate cancer is essential in improving the survival rate of patients. This study aimed to develop a machine-learning…
Effective patient treatment and care depend heavily on accurate disease diagnosis. The availability of multi-modal medical data in recent years, such as genetic profiles, clinical reports, and imaging scans, has created…
It is a highly difficult challenge to achieve correlation between images by reliable image authentication and this is essential for numerous therapeutic activities like combining images, creating tissue atlases and track…
The performance of machine learning methods in disease classification is affected by the quality of the dataset, one of which is unbalanced data. One example of health data that has unbalanced data is diabetes disease da…
