Evidence map›Paper›PMID 39730943›Full record

SynthesisSupportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer2024

Chatbots for breast cancer education: a systematic review and meta-analysis.

Shih-Jung Lin, Chin-Yu Sun, Dan-Ni Chen, Yi-No Kang, Khanh Dinh Hoang, Kee-Hsin Chen, Chiehfeng Chen

Abstract readSystematic ReviewMeta-Analysis
PubMed Publisher
In one paragraph

Synthesis in Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Evidence-Based Analysis of AI Chatbots in Oncology Patient Education: Implications for Trust, Perceived Realness, and Misinformation Management.Journal of cancer education : the official journal of the American Association for Cancer Education · 2025
    Review
  6. Article
  7. Article
  8. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Shih-Jung Lin *School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
Chin-Yu Sun *Department of Electronic Engineering, National Taipei University of Technology, Taipei, Taiwan.
Dan-Ni ChenDepartment of Information Technology, National Taipei University of Technology, Taipei, Taiwan.
Yi-No KangCochrane Taiwan, Taipei Medical University, Taipei, Taiwan.
Khanh Dinh HoangDepartment of Histopathology, Hai Phong University of Medicine and Pharmacy, Hai Phong, Vietnam.
Kee-Hsin Chen *Cochrane Taiwan, Taipei Medical University, Taipei, Taiwan.
Chiehfeng Chen *Cochrane Taiwan, Taipei Medical University, Taipei, Taiwan. clifchen@tmu.edu.tw.ORCID http://orcid.org/0000-0002-1595-6553

Funding

National Taipei University of Technology and Wan Fang Hospital, Taipei Medical University Joint Research Program NTUT-WFTMU-112-01
6 · The paper itself

Abstract

backgroundEffective education and awareness regarding breast cancer are critical. Traditional educational methods often fail to meet the diverse information needs of patients. Patients should be provided with tailored, accessible information to improve their retention and understanding of disease-related information. PURPOSE: This systematic review and meta-analysis evaluated the effectiveness of chatbots for providing breast cancer education. By examining patient satisfaction with and the usability and efficacy of chatbot interventions, this study seeks to support the integration of chatbot technology into cancer education.

methodsThis review, which was conducted in accordance with PRISMA guidelines, included studies from MEDLINE, Embase, and the Cochrane Library up to May 2024. The main inclusion criterion was chatbot interventions for breast cancer education. Meta-analysis was performed using Review Manager and Open Meta-Analyst software.

resultsOf the 208 articles initially identified, 6 studies met the inclusion criteria, involving a total of 1342 women with early-stage or at-risk hereditary breast cancer. The meta-analysis revealed that most participants (85 to 99%) reported high satisfaction with chatbot interventions for breast cancer education, with no significant differences in satisfaction compared to genetic counselors or physicians. The chatbot interventions also showed positive effects on knowledge acquisition (mean proportion = 90.8%) and alleviated patients' symptoms significantly more than routine care.

conclusionThis study demonstrated that chatbots can effectively provide personalized and interactive educational support, enhancing patients' understanding and retention of disease-related information. The integration of chatbot technology into educational programs can empower patients, ultimately promoting breast cancer awareness and prevention.

Indexed as

Breast NeoplasmsPatient Education as TopicPatient SatisfactionFemaleHumansBreast cancerChatbotsHealthcare

Identifiers

PMID39730943

What OpenQuestion holds

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Registered trials

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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.