REVIEW article
Front. Oncol.
Sec. Cancer Epidemiology and Prevention
This article is part of the Research TopicStrategies to Improve Awareness and Management of Cancer Risk Factors and ScreeningsView all 12 articles
Enhancing Cancer Risk Awareness and Screening Management through Artificial Intelligence: A Narrative Review
Provisionally accepted
- 1West China Hospital, Sichuan University, Chengdu, China
- 2Shandong First Medical University, Jinan, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Cancer is a major public health problem worldwide, and early detection through risk awareness and screening is critical for improving patient outcomes. Although modern medicine has made certain progress, there are still many unmet clinical needs in areas such as precise diagnosis, precise treatment and risk assessment.Traditional strategies to promote public awareness and optimize screening programs face persistent challenges. With the development of modern science and technology, artificial intelligence (AI) has gradually become an important force driving innovation in the field of oncology.Recent advances in artificial intelligence, particularly large language models (LLMs), have introduced new opportunities to address these barriers by enabling personalized risk communication, predictive analytics, and automated decision support. By summarizing recent advances in the application of artificial intelligence to early cancer detection, this review seeks to propose innovative strategies for early screening and precise diagnosis, ultimately aiming to reshape the landscape of cancer prevention and treatment.
Keywords: artificial intelligence, Cancer, Early Screening, Precise diagnosis, Management
Received: 30 Aug 2025; Accepted: 30 Oct 2025.
Copyright: © 2025 Wu, Tang, Zheng, Gu, Ma, Xian, Mao, Gan and Ji. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Hui Mao, merrymh@126.com
Jiadi Gan, med_ganjd@163.com
Guiyi Ji, guiyi01@wchscu.cn
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.