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 ever-increasing capabilities of deep learning for image analysis and recognition have encouraged some researchers investigates potential benefits of merging Hyperspectral Images (HSI) and Geographic Information Syste…
By 2030, chronic obstructive pulmonary disease (COPD) is expected to become one of the top three causes of death and a leading contributor to illness globally. Chronic Obstructive Pulmonary Disease (COPD) is a debilitati…
Heart disease (HD) continues to rank as the top cause of morbidity and mortality worldwide, prompting the enormous importance of correct prediction for effective intervention and prevention strategies. The proposed resea…
Accurate segmentation of chest X-rays is essential for effective medical image analysis, but challenges arise due to inherent stability issues caused by factors such as poor image quality, anatomical variations, and dise…
The adoption of cloud computing in healthcare holds the potential to revolutionize healthcare delivery, particularly in developing regions. Despite its promise of scalability, cost-effectiveness, and improved data manage…
Accurate and efficient diagnostic methods are essential for crop health monitoring due to the substantial impact of tomato leaf diseases on crop yield and quality. Traditional machine learning models, such as convolution…
Human monkeypox is a persistent global health challenge, ranking among the most common illnesses worldwide. Early and accurate diagnosis is critical to developing effective treatments. This study proposes a comprehensive…
Rice plays a vital role in the food stock. But sometimes this crop leaf falls into disease. And, the amount of food consumed will decrease due to leaf disease. So, discovering the rice leaf disease is necessary to improv…
Integrating Internet of Things (IoT) with Artificial Intelligence (AI) is one of the catalysts for improving traditional healthcare services. This integration has created many opportunities that have led to healthcare sh…
Accurate forecasting of infectious disease spread is essential for effective resource planning and strategic decision-making in public health. This study provides a comprehensive evaluation of various machine learning mo…
