Sex-Based Bias in Artificial Intelligence-Based Segmentation Models in Clinical Oncology.

Journal: Clinical oncology (Royal College of Radiologists (Great Britain))
PMID: 39874747

Abstract

Artificial intelligence (AI) advancements have accelerated applications of imaging in clinical oncology, especially in revolutionizing the safe and accurate delivery of state-of-the-art imaging-guided radiotherapy techniques. However, concerns are growing over the potential for sex-related bias and the omission of female-specific data in multi-organ segmentation algorithm development pipelines. Opportunities exist for addressing sex-specific data as a source of bias, and improving sex inclusion to adequately inform the development of AI-based technologies to ensure their fairness, generalizability and equitable distribution. The goal of this review is to discuss the importance of biological sex for AI-based multi-organ image segmentation in routine clinical and radiation oncology; sources of sex-based bias in data generation, model building and implementation and recommendations to ensure AI equity in this rapidly evolving domain.

Authors

  • F X Doo
    University of Maryland Medical Intelligent Imaging (UM2ii) Center, Department of Radiology and Nuclear Medicine, University of Maryland, Baltimore, MD, USA; University of Maryland-Institute for Health Computing (UM-IHC), University of Maryland, North Bethesda, MD, USA.
  • W G Naranjo
    Department of Medical Physics, Columbia University, New York, New York, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • T Kapouranis
    Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • M Thor
    Memorial Sloan Kettering Cancer Center, New York, New York, USA.
  • M Chao
    Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • X Yang
    Department of Urology, Affiliated Hospital of Qingdao University, Qingdao, China.
  • D C Marshall
    Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, New York, USA. Electronic address: deborah.marshall@mountsinai.org.

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