Sustainable, Safe, and Eco-Conscious AI for Medical Image Analysis

Artificial intelligence (AI) and machine learning (ML) have significantly advanced medical image analysis by enhancing diagnostic accuracy and improving workflow efficiency. However, these innovations also introduce new challenges. Modern AI models require substantial computational resources, leading to high energy consumption, increased carbon emissions, and hardware waste. Additionally, large-scale data storage and cloud-based processing further contribute to the environmental burden. At the same time, growing concerns about safety and ethics must not be overlooked. Algorithmic biases can reinforce healthcare disparities, while inadequate data governance and security measures may compromise patient privacy. These issues highlight the urgent need to develop AI/ML systems that are not only high-performing but also sustainable, safe, and ethically sound. Ensuring that medical imaging technologies align with environmental and societal values has become a key priority for researchers, developers, and healthcare providers alike.

As AI becomes increasingly integrated into all aspects of medical imaging, there is an urgent need to ensure that these technologies are developed and deployed in a sustainable, safe, and eco-friendly manner. The carbon footprint of training and operating large-scale AI models is significant—data centers supporting healthcare AI may already account for around 1% of global electricity use—and this figure is expected to rise. Additionally, if concerns such as algorithmic bias, transparency, and patient privacy are not proactively addressed, public trust and clinical adoption may be compromised. This Research Topic aims to tackle these challenges by uniting multidisciplinary research on green, ethical, and safe AI/ML in medical imaging. It will explore strategies to reduce energy and resource demands (e.g., model quantization, pruning, efficient hardware), while promoting frameworks that ensure robustness, fairness, and data governance compliance. The ultimate goal is to foster AI systems that balance high performance with minimal environmental impact and maximal societal benefit.

This Research Topic welcomes contributions across all medical imaging modalities (e.g., radiology, pathology, ultrasound), with a focus on sustainable, safe, and eco-conscious AI/ML-based image analysis. We encourage submissions that address, but are not limited to, the following themes: Energy-efficient algorithms and models for medical image analysis, including techniques such as model compression, pruning, low-power AI hardware, and green computing paradigms. Eco-conscious imaging workflows and resource optimization strategies that aim to reduce redundant imaging, minimize energy consumption, and limit electronic waste. Ethical and safe AI practices in medical imaging, covering bias mitigation, transparency, explainability, and clinical safety considerations. Data governance and privacy, including privacy-preserving learning approaches (e.g., federated learning) and alignment with data protection regulations. Lifecycle sustainability of AI systems, encompassing carbon footprint assessments, sustainable deployment strategies, and policy frameworks that promote environmentally responsible innovation in medical imaging.

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
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• ... 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
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review

Keywords: Sustainable AI, Green Computing, Medical Imaging, Energy Efficiency, Data Governance

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.