Artificial intelligence in women’s cancers: innovation and challenges in clinical translation
Cet article examine les innovations et les défis à relever concernant les technologies de l'intelligence artificielle pour la prise en charge des cancers gynécologiques et la recherche clinique
Artificial intelligence (AI) is set to transform the care of women with cancer. From early detection via digital phenotyping to diagnosis, treatment, and follow-up, innovative AI applications are rapidly emerging across the cancer care continuum. AI-assisted mammographic screening for breast cancer has been clinically translated, and AI-based contouring in radiotherapy is streamlining treatment planning and improving consistency of cancer care. Research areas include radiomic analysis for ovarian tumour characterisation, machine learning for endometrial cancer subtyping, and automated assessment for cervical cancer screening. Challenges such as data scarcity and tumour heterogeneity in gynaecological cancers hinder the development of robust AI models, a problem further compounded by the limited availability of large, prospective validation cohorts. Emerging generative AI and multimodal AI systems hold promise to address these limitations by leveraging large-scale, diverse training datasets. Building trust in AI systems will require rigorous prospective real-life validation, regulatory oversights, and well-defined legal frameworks. A key opportunity exists to develop inclusive, clinically meaningful AI devices across all women?s cancers, driven by rapid advances in AI in health care and strengthened by national and international initiatives promoting health-care innovation. Through multidisciplinary collaboration, AI has the potential to move beyond research and help in early diagnoses and provide personalised treatment strategies. In this Series paper, we review AI developments in breast and gynaecological cancers, including applications in clinical adoption and those actively being developed to address unmet needs in early detection, characterisation, treatment, and prognostication.
The Lancet Digital Health , résumé, 2025