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
Addressing the challenges of diagnosing lower respiratory tract infections, this study unveils the potential of Deep Convolutional Neural Networks (Deep CNN) as transformative tools in medical image interpretation. Our r…
Improving medical image quality is essential for accurate diagnosis, treatment planning, and ongoing condition monitoring. A crucial step in many medical applications, the restoration of damaged input images tries to ret…
In recent years, significant advancements have been made in the realm of plant disease classification, with a particular focus on leveraging the capabilities of deep learning techniques. This study delves into the utiliz…
Gene expression data has emerged as a crucial aspect of big data in genomics. The advent of high-throughput technologies such as microarrays and next-generation sequencing has enabled the generation of extensive gene exp…
Self-supervised learning (SSL) is a type of machine learning that does not require labeled data. Instead, SSL algorithms learn from unlabeled data by predicting the order of image patches, predicting the missing pixels i…
The image compression techniques are the fast-growing methods and have developed on large scale. Among them, wavelet-based compression methods are most promising and efficient techniques widely used in the field of medic…
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IJACSA`s Publication Principles. We hereby retract the content of t…
The work of the Ophthalmologist in manually detecting specific eye related disease is challenging especially screening through large volume of dataset. Deep learning models can leverage on medical imaging like the retina…
Cocoa cultivation is of immense importance to the people of Côte d'Ivoire. However, this culture is experiencing significant challenges due to diseases spread by various agents such as bacteria, viruses, and fungi, which…
Heart disease is the leading cause of mortality worldwide. Early identification and prediction can play a crucial role in preventing and treating it. Based on patient data, machine learning techniques may be used to cons…
