AI-Powered Biomedical Image Analysis

Dear Colleagues,

Artificial intelligence (AI) has become a transformative force in biomedical image analysis, offering unprecedented opportunities to enhance disease diagnosis, prognosis, and treatment planning. With the rapid evolution of deep learning, AI-driven approaches have demonstrated superior performance in extracting clinically relevant patterns from complex imaging data, highlighting their promise for future precision medicine.

Biomedical imaging encompasses diverse modalities, including magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), ultrasound, and whole-slide pathology images. Each modality captures unique biological and structural information, enabling tasks such as lesion detection, organ segmentation, disease classification, image registration, and quantitative biomarker extraction. AI not only accelerates these processes but also improves reproducibility and reduces observer variability, thereby facilitating more reliable clinical decision-making.

Recent advancements are driving the next wave of innovation. Self-supervised learning enables effective model training with limited annotations, while foundation models trained on massive datasets provide strong generalization across imaging modalities and clinical tasks. Large language models (LLMs) and vision–language models (VLMs) are opening new opportunities for medical image captioning, report generation, and cross-modal reasoning, bridging the gap between imaging data and clinical narratives. Multimodal learning further integrates imaging with genomics, electronic health records, and other biomedical signals, enabling a holistic understanding of disease processes. Together, these approaches are pushing biomedical image analysis toward more robust, scalable, and clinically meaningful solutions.

This Special Issue aims to showcase cutting-edge research on AI-powered biomedical image analysis, spanning novel algorithms, innovative applications, and translational studies bridging the gap between computational advances and clinical practice. By bringing together interdisciplinary contributions, the issue seeks to highlight both technical innovations and their implications for real-world healthcare.

Dr. Lei Fan
Guest Editor

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