ReviewDigital health
Leveraging AI chatbots in supporting breast and ovarian cancer patients: A scoping review.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Individuals who are at risk of or diagnosed with breast and ovarian cancer often face barriers in seeking help due to embarrassment, complex information needs, and limited access to timely support. Chatbots provide a private, non-judgmental communication interface that may help overcome these barriers. This scoping review systematically examines chatbot applications in breast and ovarian cancer care, focusing on their design, functionalities, evaluation outcomes, and implementation barriers and facilitators. Methods: A comprehensive search was conducted through PubMed, Medline, Embase, CINAHL, Cochrane Library, and Web of Science databases using keywords related to chatbots, breast cancer, and ovarian cancer. Studies were screened and reviewed following PRISMA-ScR guidelines. Results: Nineteen studies met the inclusion criteria, covering educational support (n=16), clinical support (n=14), and psychosocial support (n=11). NLP served as the primary technical foundation (n=12), with rule-based and retrieval-based approaches equally represented (n=7 each) and a growing adoption of LLM-driven approaches (n=5). Studies reported consistently high user satisfaction, though RCT evidence on clinical efficacy showed mixed results with benefits varying by patient subgroups. Qualitative studies identified adoption barriers including data privacy concerns and patient expectations exceeding chatbot scope. While breast cancer applications showed promising outcomes, research on diagnosed ovarian cancer patients remains limited. Conclusions: Chatbots show potential as complementary tools in breast and ovarian cancer care. Future development should focus on standardised evaluation frameworks, specialized ovarian cancer applications, and optimizing the balance between automation and human oversight in clinical settings.
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