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Artificial intelligence in women's cancers : innovation and challenges in clinical translation

Rockall, Andrea G. ; Chiu, Selina My ; Aboagye, Eric O. ; Dustler, Magnus LU orcid ; Fotopoulou, Christina ; Ghaem-Maghami, Sadaf ; Taylor, Alexandra and Zackrisson, Sophia LU (2025) In The Lancet. Digital health 7(10). p.100940-100940
Abstract

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... (More)

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.

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author
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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
The Lancet. Digital health
volume
7
issue
10
pages
1 pages
publisher
Elsevier
external identifiers
  • pmid:41241582
  • scopus:105024016174
ISSN
2589-7500
DOI
10.1016/j.landig.2025.100940
language
English
LU publication?
yes
additional info
Publisher Copyright: Copyright © 2025 The Author(s). Published by Elsevier Ltd.. All rights reserved.
id
ba0de7f6-a058-44cf-a247-83bb23c2eebe
date added to LUP
2026-02-23 16:00:40
date last changed
2026-02-24 03:22:45
@article{ba0de7f6-a058-44cf-a247-83bb23c2eebe,
  abstract     = {{<p>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.</p>}},
  author       = {{Rockall, Andrea G. and Chiu, Selina My and Aboagye, Eric O. and Dustler, Magnus and Fotopoulou, Christina and Ghaem-Maghami, Sadaf and Taylor, Alexandra and Zackrisson, Sophia}},
  issn         = {{2589-7500}},
  language     = {{eng}},
  month        = {{10}},
  number       = {{10}},
  pages        = {{100940--100940}},
  publisher    = {{Elsevier}},
  series       = {{The Lancet. Digital health}},
  title        = {{Artificial intelligence in women's cancers : innovation and challenges in clinical translation}},
  url          = {{http://dx.doi.org/10.1016/j.landig.2025.100940}},
  doi          = {{10.1016/j.landig.2025.100940}},
  volume       = {{7}},
  year         = {{2025}},
}