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
Artificial intelligence is driving digital transformation across multiple sectors, including healthcare, pharmaceuticals, industrial production, and the automotive industry. In healthcare specifically, AI-powered predict…
In recent years, artificial intelligence (AI) has transformed numerous sectors, including healthcare, and ophthalmology is no exception. The field has seen remarkable progress in using AI to detect, diagnose, and manage…
Medical image compression is an active research area owing to the growth of the volume of medical image data in digital form. A method for lossless compression of medical images was proposed using logic minimization. The…
Robust medical image reconstruction is a critical requirement for accurate diagnosis and clinical decision-making, particularly when images are affected by degradation, noise, or low resolution. Conventional encoder–deco…
The development of healthcare data performance analysis is becoming more driven by the incorporation of intelligent computing paradigms that guarantee real-time, scalable, and personalized feedback for coaches and athlet…
Client selection remains a critical challenge in Federated Learning (FL). Resource-aware strategies aim to reduce training delays and mitigate stragglers by selecting an appropriate subset of clients in each round. Howev…
Emotion recognition is critical in the development of real-time mental health care and individualized cognitive behavior. Current strategies to recognize cognitive emotions frequently fail to capture complex time depende…
Interoperability across heterogeneous information systems remains a persistent challenge, particularly in resource constrained contexts where infrastructures are fragmented and data formats remain incompatible. This stud…
Cardiopathy is one of the most serious diseases worldwide with its high morbidity and mortality rates posing a latent risk over time. The objective of this research focuses on evaluating Machine Learning (ML) models such…
As living standards rise, people are paying increasing attention to health. Vast quantities of medical data are generated daily, yet each piece contains sensitive information such as patients’ names, mobile numbers, emai…
