ArticleSupportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer2025
AI-driven patient support: Evaluating the effectiveness of ChatGPT-4 in addressing queries about ovarian cancer compared with healthcare professionals in gynecologic oncology.
Article in Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Leveraging artificial intelligence to streamline documentation and support patient-centered gynecologic oncology outpatient visits.Gynecologic oncology reports · 2026Article
- New Technologies and Digital Health Tools in Patients With Solid Tumors and Hematological Malignancies: Cross-Sectional Multicenter Survey Study.JMIR cancer · 2026Article
- Leveraging AI chatbots in supporting breast and ovarian cancer patients: A scoping review.Digital healthReview
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Authors and funding
10 authors.
Funding
Abstract
purposeArtificial intelligence (AI) chatbots, such as ChatGPT-4, allow a user to ask questions on an interactive level. This study evaluated the correctness and completeness of responses to questions about ovarian cancer from a GPT-4 chatbot, LilyBot, compared with responses from healthcare professionals in gynecologic cancer care.
methodsFifteen categories of questions about ovarian cancer were collected from an online patient Chatgroup forum. Ten healthcare professionals in gynecologic oncology generated 150 questions and responses relative to these topics. Responses from LilyBot and the healthcare professionals were scored for correctness and completeness by eight independent healthcare professionals with similar backgrounds blinded to the identity of the responders. Differences between groups were analyzed with Mann-Whitney U and Kruskal-Wallis tests, followed by Tukey's post hoc comparisons.
resultsMean scores for overall performance for all 150 questions were significantly higher for LilyBot compared with the healthcare professionals for correctness (5.31 ± 0.98 vs. 5.07 ± 1.00, p = 0.017; range = 1-6) and completeness (2.66 ± 0.55 vs. 2.36 ± 0.55, p < 0.001; range = 1-3). LilyBot had significantly higher scores for immunotherapy compared with the healthcare professionals for correctness (6.00 ± 0.00 vs. 4.70 ± 0.48, p = 0.020) and completeness (3.00 ± 0.00 vs. 2.00 ± 0.00, p < 0.010); and gene therapy for completeness (3.00 ± 0.00 vs. 2.20 ± 0.42, p = 0.023).
conclusionsThe significantly better performance by LilyBot compared with healthcare professionals highlights the potential of ChatGPT-4-based dialogue systems to provide patients with clinical information about ovarian cancer.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.