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<p>Sex and gender represent critical yet underutilized precision biomarkers in oncology, influencing cancer incidence, progression, treatment response, and survival outcomes. Biological sex, characterized by chromosomal, hormonal, and anatomical factors, directly influences tumor biology, immune responses, and therapy-related toxicities. Gender, encompassing social norms and behaviors, further impacts cancer risk through lifestyle choices, health awareness, and access to healthcare. Nonetheless, the predominant portion of clinical research remains gender- and sex-neutral, hence limiting the efficacy of precision medicine. Artificial intelligence (AI) facilitates predictive modeling for diagnosis, therapy optimization, and prognosis by integrating sex- and gender-specific data, ranging from high-dimensional biological markers to actual clinical records. In conditions such as breast, prostate, and lung cancers, AI-driven patient stratification can discern sex-specific biological pathways, guide the formulation of targeted treatments, and improve therapy customization. Despite these advancements, challenges about data representation, annotation, and regulatory compliance persist. To attain equitable precision oncology, ethically governed AI frameworks must be integrated with extensive, longitudinal, and gender-balanced datasets. Consistently incorporating sex and gender across the cancer care continuum for diverse patient populations may enhance quality of life, augment therapeutic efficacy, and reduce disparities.</p>

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Artificial intelligence in cancer diagnosis and therapy using sex and gender as precision biomarkers

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月25日

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  • Arun Karnwal,
  • Aqueel-Ur Rehman,
  • Amar Yasser Jassim,
  • Gaurav Kumar,
  • Abdel Rahman Mohammad Said Al-Tawaha,
  • Natalia Nesterova

摘要

Sex and gender represent critical yet underutilized precision biomarkers in oncology, influencing cancer incidence, progression, treatment response, and survival outcomes. Biological sex, characterized by chromosomal, hormonal, and anatomical factors, directly influences tumor biology, immune responses, and therapy-related toxicities. Gender, encompassing social norms and behaviors, further impacts cancer risk through lifestyle choices, health awareness, and access to healthcare. Nonetheless, the predominant portion of clinical research remains gender- and sex-neutral, hence limiting the efficacy of precision medicine. Artificial intelligence (AI) facilitates predictive modeling for diagnosis, therapy optimization, and prognosis by integrating sex- and gender-specific data, ranging from high-dimensional biological markers to actual clinical records. In conditions such as breast, prostate, and lung cancers, AI-driven patient stratification can discern sex-specific biological pathways, guide the formulation of targeted treatments, and improve therapy customization. Despite these advancements, challenges about data representation, annotation, and regulatory compliance persist. To attain equitable precision oncology, ethically governed AI frameworks must be integrated with extensive, longitudinal, and gender-balanced datasets. Consistently incorporating sex and gender across the cancer care continuum for diverse patient populations may enhance quality of life, augment therapeutic efficacy, and reduce disparities.