Artificial intelligence (AI) is increasingly being applied in breast cancer care, yet its use across the post-diagnosis phase remains poorly mapped. This scoping review aimed to identify and categorise AI applications in post-diagnosis breast cancer care, encompassing treatment planning, treatment delivery, follow-up and surveillance, survivorship, and palliative care. Following JBI methodology and PRISMA-ScR reporting guidelines, four databases (MEDLINE, EMBASE, CINAHL, and Web of Science) were searched, identifying 3784 records. After screening and full-text assessment, 54 studies published between 2016 and 2024 were included. Machine learning was the predominant technology (81%), followed by generative AI (7%), conversational agents (6%), traditional natural language processing (4%), and data mining (2%). Follow-up and surveillance were the most represented care stage (48%), driven primarily by recurrence prediction models. Most applications were provider-focused (83%), while patient-facing tools accounted for 17% of studies and relied on either conversational agents or generative AI. No studies addressed palliative care. The evidence base was predominantly retrospective (70%) and concentrated in high-income countries (74%). Future research should prioritise prospective evaluation in clinical workflows, address unsupervised patient use of generative AI, and ensure equitable development across diverse populations and care settings.
Seripenah et al. (Thu,) studied this question.
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