Edge AI for Medical Imaging: Toward Sustainable, Explainable, and Real-Time Diagnostic Systems

Medical imaging stands at the forefront of modern healthcare, enabling accurate diagnosis, precise treatment planning, and continuous disease monitoring. With the proliferation of Artificial Intelligence (AI), particularly deep learning, remarkable progress has been made in medical image interpretation and decision support. Yet, the prevailing reliance on cloud-based infrastructures raises bottlenecks in latency, data privacy, bandwidth usage, and energy efficiency—barriers that impede real-world clinical integration. Edge Artificial Intelligence (Edge-AI) offers a transformative alternative by processing imaging data directly on or near the data acquisition device. This paradigm enables secure, low-latency, and context-aware decision-making, thereby reducing the dependence on centralized computing infrastructures. Despite its promise, the implementation of Edge-AI in medical imaging still faces challenges in balancing computational efficiency, model robustness, interpretability, and sustainability—necessitating a concerted research effort across disciplines.

This Research Topic aims to accelerate progress toward the design and deployment of intelligent, real-time, and explainable diagnostic imaging systems powered by Edge-AI. It seeks to address critical scientific and engineering questions: How can advanced deep learning models retain clinical-grade accuracy under limited computational and energy resources? What methodologies best ensure transparency and interpretability in real-time decision-making? How can Edge-AI integrate seamlessly with existing medical imaging workflows and clinical validation pipelines? By encouraging contributions grounded in both algorithmic innovation and translational relevance, this initiative aspires to bridge the gap between laboratory research and hospital-ready systems. It particularly welcomes studies that focus on sustainability, federated learning, privacy preservation, and interdisciplinary collaboration among computer scientists, clinicians, and biomedical engineers.

To gather further insights into the intersection of Edge-AI and medical imaging for real-world diagnostic systems, we welcome articles addressing, but not limited to, the following themes:

-- Real-time and edge-based medical image analysis for diagnostics
-- Explainable, interpretable, and trustworthy Edge-AI frameworks for healthcare
-- Energy-efficient and sustainable AI model design and deployment strategies
-- Federated and privacy-preserving learning in medical data ecosystems
-- Multimodal imaging analytics and integration under Edge-AI architectures
-- Hardware-aware AI optimization for resource-limited or on-device processing
-- Scalable and clinically validated Edge-AI frameworks for diagnostic imaging applications

We invite submissions of Original Research, FAIR2 Articles, Reviews, Methods, and Perspective articles that demonstrate innovation, clinical applicability, and reproducibility in advancing next-generation sustainable and explainable imaging intelligence.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review
• Technology and Code

Keywords: Medical Imaging; Edge AI, Explainable AI, Image Analysis, Diagnostic Imaging, Deep Learning, Image Segmentation, Clinical Decision Support, Privacy-Preserving AI, Sustainable Computing

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.