Special Issue Information
The rapid growth of biomedical data—driven by advances in genomics, medical imaging, electronic health records, wearable sensors, IoT, digital twins, and robotics—has created tremendous opportunities to improve human health. At the same time, the volume, complexity, and heterogeneity of such data present critical challenges for meaningful interpretation and clinical application.
Artificial intelligence (AI), including machine learning (ML), has emerged as a powerful tool to address these challenges. AI-based approaches are increasingly applied to disease diagnosis, drug discovery, personalized medicine, and epidemiology, enabling data-driven decision-making and prediction.
This Special Issue seeks to highlight the latest AI and ML applications in biomedical data analysis, and to explore the associated methodological and practical challenges in modern healthcare. We particularly welcome contributions that address:
• The use of advanced models such as deep learning, transformer architectures, self-supervised and multi-modal learning;
• Efforts to overcome key barriers, including data heterogeneity, lack of annotated datasets, interpretability, algorithmic bias, and trustworthiness of AI systems;
• Novel computational frameworks, evaluation methodologies, and interdisciplinary case studies.
With the exponential increase in biomedical data, there is an urgent need for robust and transparent AI tools that can generate actionable insights and improve clinical outcomes. This Special Issue provides a timely platform for researchers and practitioners to share innovations that can help shape the future of AI in biomedicine.
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