Previously submitted to: JMIR Cancer (no longer under consideration since Jun 02, 2026)
Date Submitted: Oct 20, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Applications of Artificial Intelligence (AI) in Breast Cancer Care Delivery and Education: A Scoping Review.
ABSTRACT
Background:
AI is revolutionising healthcare, particularly for breast cancer, which is a complex global health issue. AI offers innovative solutions for personalising care, optimising treatment plans, and improving prognostic predictions, which are crucial for managing patient needs.
Objective:
This scoping review aims to map the existing literature on artificial intelligence (AI) applications used to educate and manage patients with breast cancer during the post-diagnosis phase of care.
Methods:
The review followed the Joanna Briggs Institute methodology for scoping reviews and was reported according to PRISMA-ScR guidelines. MEDLINE, EMBASE, CINAHL, and Web of Science were searched from January 2013 to December 2024. Eligible studies involved patients with breast cancer or healthcare providers using AI tools to enhance patient education, communication, or care management after diagnosis. Non-empirical publications, studies focused solely on screening or diagnosis, and research on non-human subjects were excluded. Two reviewers independently screened, selected, and extracted data, and findings were synthesised narratively.
Results:
Of 3,788 records identified from the search, 58 studies were included in the review. Machine Learning was the most prevalent AI form applied in prognosis prediction, treatment optimization, and patient education. AI chatbots and Natural Language Processing (NLP) technologies showed potential in improving patient engagement and facilitating effective communication. AI-driven data mining identified treatment pathways in early breast cancer.
Conclusions:
AI technologies, particularly machine learning, chatbots, and NLP, have significant potential in enhancing breast cancer care through personalized management and patient education. However, disparities in AI research and application highlight the need for thoughtful expansion of these technologies globally. Future research should focus on diversifying AI applications in breast cancer care, emphasizing patient-centred approaches and qualitative insights to navigate the complexities of patient and healthcare provider experiences.
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Copyright
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