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Artificial intelligence (AI) in oncology imaging must address sex-specific data to prevent bias. Ensuring female data inclusion is crucial for fair and equitable AI development in radiation oncology.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • AI enhances clinical oncology imaging and radiotherapy.
  • Growing concerns exist regarding sex-related bias in AI algorithms.
  • Female-specific data is often omitted in multi-organ segmentation development.

Purpose of the Study:

  • Review the importance of biological sex in AI-based multi-organ segmentation for oncology.
  • Identify sources of sex-based bias in AI development and implementation.
  • Provide recommendations for ensuring AI equity in this field.

Main Methods:

  • Literature review focusing on AI, medical imaging, and oncology.
  • Analysis of data generation, model building, and implementation processes.
  • Discussion of sex as a variable in AI algorithm development.

Main Results:

  • Biological sex is a critical factor influencing AI segmentation accuracy.
  • Sex-based bias can arise from data collection, algorithm design, and deployment.
  • Current AI models may not generalize equitably across sexes.

Conclusions:

  • Addressing sex-specific data is vital for unbiased AI in oncology.
  • Improved sex inclusion is necessary for fair and generalizable AI technologies.
  • Recommendations are proposed to promote AI equity in imaging-guided radiotherapy.