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A Study on Energy Consumption in AI-Driven Medical Image Segmentation.

R Prajwal1, S J Pawan1, Shahin Nazarian2

  • 1Radiomics Lab, University of Southern California, Los Angeles, CA 90033, USA.

Journal of Imaging
|June 25, 2025
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Summary

This study reveals the energy demands of artificial intelligence (AI) in medical image analysis. Depthwise Convolution with Mixed Precision offers the most energy-efficient AI training for medical imaging tasks.

Keywords:
artificial intelligencemedical image segmentationsustainability

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Area of Science:

  • Medical Image Analysis
  • Artificial Intelligence
  • Sustainable Computing

Background:

  • The environmental impact of artificial intelligence (AI) in medical image analysis is under-explored.
  • AI models, particularly for segmentation tasks, require significant computational resources.
  • Understanding energy consumption is crucial for sustainable AI development in healthcare.

Purpose of the Study:

  • To analyze the energy demands of AI workflows for medical image segmentation.
  • To compare energy consumption between AI training and inference phases.
  • To evaluate the impact of different convolutional variants and optimization techniques on energy efficiency.

Main Methods:

  • Utilized the Kidney Tumor Segmentation-2019 (KiTS-19) dataset for analysis.
  • Evaluated Standard Convolution, Depthwise Convolution, and Group Convolution.
  • Assessed optimization techniques including Mixed Precision and Gradient Accumulation.
  • Measured energy consumption focusing on computational complexity, memory access, and I/O operations.

Main Results:

  • AI training is energy-intensive, but inference's cumulative energy use can be higher over a model's lifecycle.
  • Depthwise Convolution combined with Mixed Precision demonstrated the lowest training energy consumption with strong performance.
  • Group Convolution exhibited poor energy efficiency due to substantial input/output overhead.
  • Inference energy consumption is significantly influenced by recurring computational demands.

Conclusions:

  • Depthwise Convolution with Mixed Precision presents the most energy-efficient configuration for medical image segmentation AI.
  • GPU-centric strategies and energy-conscious AI practices are essential for sustainable medical imaging AI.
  • Actionable guidance is provided for developing scalable and environmentally responsible AI in medical image analysis.