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
The identification and diagnosis of liver diseases hold significant importance within the domain of digital pathology research. Various methods have been explored in the literature to address this crucial task, with deep…
Globally, lung cancer remains the leading cause of cancer-related deaths, with early detection significantly improving survival rates. Developing robust machine learning models for early detection necessitates access to…
Using the Arduino platform under the Internet of Things (IoT) platform to diagnose individuals at risk of heart diseases. An enormous volume of data focus has been placed on delivering high-quality healthcare in response…
This research investigates the application of open-source transformers, specifically the ConvNeXt V2 and Seg-former models, for brain tumor classification and segmentation in medical imaging. The ConvNeXt V2 model is ada…
Occupational diseases present a significant global challenge, affecting a vast number of workers. Accurate prediction of occupational disease incidence is crucial for effective prevention and control measures. Although d…
Deep learning models known as Generative Adversarial Networks (GANs) have shown great potential in several applications, such as computer vision and image synthesis. They are now a viable tool in medical imaging, useful…
“Food is the most important thing for the people”, Food is intricately linked to both the national economy and the livelihood of the people, serving as a vital material for our daily existence. Wheat, standing as one of…
Early illness diagnosis, treatment monitoring, and healthcare administration all depend heavily on the identification of abnormalities in medical data. This paper proposes a unique way to improve healthcare anomaly detec…
Ethereum has gained significant attention from businesses as a blockchain technology since its conception. Beyond the first use of cryptocurrencies, it provides many additional features. In the pharmaceutical sector, whe…
With the recent impact of viral infections and pandemics – akin to a recent global healthcare emergency due to COVID-19 - there is an urgent need for mass-scale testing and vaccination initiatives for tackling the health…
