Artificial Intelligence Methods for Biomedical Data Processing

Dear Colleagues,

This Special Issue aims to explore the latest advancements in applying artificial intelligence (AI) techniques to the processing and analysis of biomedical data. Biomedical data science has experienced rapid growth in recent years, with AI playing a critical role in transforming raw data into actionable insights. This Special Issue is designed to showcase cutting-edge research that enhances AI's application in biomedical contexts while addressing the challenges and opportunities of processing complex, variable, and large volumes of data.

The focus of this Special Issue is on AI methodologies, including machine learning, deep learning, and natural language processing, that are specifically tailored to process biomedical data. These applications span genomics, medical imaging, patient health records, sensor data, and drug discovery. Additionally, the Special Issue will highlight innovative approaches that integrate AI into clinical practice, such as predictive analytics, disease detection, and personalized treatment plans. The scope covers both theoretical and practical aspects, ranging from novel algorithms and models to real-world applications and case studies. This includes implementing AI solutions in healthcare settings, and addressing ethical, regulatory, and technological challenges. The purpose is to foster interdisciplinary collaboration among AI researchers, biomedical scientists, and healthcare practitioners. By bridging the gap between AI research and real-world applications, this Special Issue seeks to inspire new research directions and enhance the translation of innovative AI solutions into clinical and biomedical environments.

In relation to existing literature, this Special Issue will serve to supplement the growing body of work on AI in biomedical data processing. While much of the current literature focuses on individual applications, such as AI in genomics or medical imaging, this Special Issue will provide a more comprehensive exploration of how AI can process diverse biomedical data types from genomic sequences to real-time patient data captured by wearable devices. Furthermore, by addressing the ethical implications, usability challenges, and regulatory issues of applying AI in these domains, it will offer a unique perspective that expands on the limitations of current studies. As a result, this Special Issue will provide a valuable resource for researchers and practitioners alike, contributing to the ongoing evolution of AI’s role in biomedical data processing.

We look forward to your contributions, which will help advance this critical field and inspire future innovations in AI-driven biomedical data solutions.

Dr. Michael Winter
Guest Editor

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