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Characterizing browser-based medical imaging AI with serverless edge computing: towards addressing clinical data

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This study introduces a privacy-preserving system for medical imaging AI using serverless edge computing. It enables AI deployment on standard hardware, protecting patient health information (PHI) and facilitating clinical integration.

Keywords:
Clinical Data PrivacyMachine LearningMedical Image ProcessingWeb Development

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Area of Science:

  • Medical Imaging AI
  • Edge Computing
  • Data Privacy

Background:

  • Artificial intelligence (AI) is increasingly used in medical imaging for tasks like disease visualization and decision support.
  • Deploying AI via cloud computing raises significant patient health information (PHI) privacy concerns, limiting clinical adoption.
  • Serverless edge computing offers a solution by enabling privacy-preserving AI model distribution with flexibility and security.

Purpose of the Study:

  • To propose a novel browser-based, cross-platform system for deploying AI in medical imaging.
  • To ensure privacy preservation through serverless edge computing on consumer-level hardware.
  • To implement and evaluate a 3D medical image segmentation model for lung cancer screening.

Main Methods:

  • Developed a browser-based, cross-platform AI deployment system utilizing serverless edge computing.
  • Implemented a 3D convolutional neural network (CNN) for computed tomography (CT) based lung cancer screening.
  • Characterized system performance (runtime, memory usage) across different operating systems and browsers.

Main Results:

  • Successfully deployed a 3D CNN model for CT lung cancer screening.
  • Achieved an average runtime of 80 seconds on major browsers (excluding Safari) and 210 seconds on Safari.
  • Demonstrated average memory usage of 1.5 GB across Windows, Linux, and Mac platforms.

Conclusions:

  • The proposed system offers a privacy-preserved solution for medical imaging AI, minimizing PHI exposure risks.
  • The framework facilitates the integration of advanced deep learning methods into routine clinical practice.
  • Characterization of tools, architectures, and parameters supports broader adoption and translation of AI in healthcare.