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The Role of Artificial Intelligence in Breast Cancer Patient Education: Scoping Review

2025·0 Zitationen·Archives of Breast CancerOpen Access
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5

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2025

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Abstract

Background: Breast cancer is one of the most common cancers worldwide. Effective patient education is essential to improve understanding of diagnosis, treatment options, and self-care. Advances in artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT, offer opportunities to deliver interactive, personalized health education. Methods: This study used a scoping review approach guided by the Arksey and O’Malley framework and Joanna Briggs Institute recommendations and is reported in accordance with the PRISMA-ScR checklist. Literature searches were conducted in PubMed, ProQuest, and Scopus for full-text, English-language articles published between January 2020 and July 2025. The PRISMA flowchart was used to document study selection. Results: Fourteen studies met the inclusion criteria, involving patients with breast cancer at different stages, women in screening programs, and expert evaluators. Eight studies directly included patients, totaling 3417 participants. Interventions comprised chatbots/LLMs, messaging platforms, and QR code modules. Most studies showed improvements in knowledge, comprehension, and satisfaction. Randomized controlled trials demonstrated gains in knowledge, reduced chemotherapy side effects, better self-care, and lower preoperative anxiety, though some effects varied by subgroup. Additional findings reported enhanced readability of medical information and moderate to high accuracy of AI responses, with variability underscoring the need for clinical oversight. Conclusion: AI-based technologies are promising for breast cancer education by improving understanding, reducing anxiety, and supporting self-care. Successful implementation requires clinical oversight, inclusivity, and adherence to evidence-based standards.

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AI in cancer detectionArtificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging
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