Artificial Intelligence Applications in Biomedical Research and Health Information Management

Abstract

Artificial Intelligence (AI) is increasingly transforming biomedical research and health information management by enhancing data processing, decision-making, and knowledge discovery in healthcare systems. This paper examines the applications of AI in biomedical research and health information management, with emphasis on its contributions to disease diagnosis, drug development, clinical decision support, and healthcare data organisation. AI technologies such as machine learning, deep learning, natural language processing, and predictive analytics are now widely used to analyse complex biomedical datasets, including genomic sequences, medical imaging, and electronic health records. In biomedical research, AI accelerates hypothesis generation, improves accuracy in disease prediction, and supports the development of personalised medicine by identifying patterns in large-scale biological data. In health information management, AI enhances the efficiency, accuracy, and accessibility of health records, improves data classification, and supports real-time clinical decision-making. The study highlights that AI reduces human error, improves operational efficiency, and strengthens evidence-based healthcare delivery. However, challenges such as ethical concerns, data privacy issues, algorithmic bias, inadequate infrastructure, and shortage of skilled personnel hinder its full adoption, especially in developing countries. Despite these limitations, AI remains a powerful tool with the potential to revolutionise biomedical research and health information systems if properly integrated into healthcare policies and practices. The study concludes that effective implementation of AI can significantly improve healthcare outcomes, research productivity, and information management efficiency.