Evidence map›Paper›PMID 37567976›Full record

Trial reportAnnals of surgical oncology2023

A Randomized Trial Comparing the Effectiveness of Pre-test Genetic Counseling Using an Artificial Intelligence Automated Chatbot and Traditional In-person Genetic Counseling in Women Newly Diagnosed with Breast Cancer.

Zahraa Al-Hilli, Ryan Noss, Jennifer Dickard, Wei Wei, Anna Chichura, Vincent Wu, Kayla Renicker, Holly J Pederson, Charis Eng

Abstract readRandomized Controlled Trial
PubMed Publisher
In one paragraph

Trial report in Annals of surgical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 3 pooled it
16.6field-weighted citation impact, top 1% of its field
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

30 citing papers in PubMed, 3 syntheses or guidelines pooled it, 53 citations in OpenAlex.

  1. Pooled it
  2. Chatbots for breast cancer education: a systematic review and meta-analysis.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024
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  7. Article
  8. Artificial intelligence in clinical genetics: Current practice and attitudes among the clinical genetics workforce.Genetics in medicine : official journal of the American College of Medical Genetics · 2026
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  18. Large-Scale Population Screening forBreast care (Basel, Switzerland) · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Zahraa Al-HilliDepartment of General Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH, USA. alhillz@ccf.org.
Ryan NossCenter for Personalized Genetic Healthcare, Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, USA.
Jennifer DickardDepartment of General Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH, USA.
Wei WeiDepartment of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA.
Anna ChichuraDepartment of General Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH, USA.
Vincent WuDepartment of General Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH, USA.
Kayla RenickerDepartment of General Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH, USA.
Holly J PedersonDepartment of General Surgery, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, OH, USA.
Charis EngCenter for Personalized Genetic Healthcare, Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, USA.
Cleveland Clinic · USCase Western Reserve University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlternative service delivery models are critically needed to address the increasing demand for genetics services and limited supply of genetics experts available to provide pre-test counseling.

methodsWe conducted a prospective randomized controlled trial of women with stage 0-III breast cancer not meeting National Comprehensive Cancer Network (NCCN) criteria for genetic testing. Patients were randomized to pre-test counseling with a Chatbot or a certified genetic counselor (GC). Participants completed a questionnaire assessing their knowledge of breast cancer genetics and a survey assessing satisfaction with their decision regarding pre-test counseling.

resultsA total of 39 patients were enrolled and 37 were randomized to genetic counseling with an automated Chatbot or a GC; 19 were randomized to Chatbot and 18 to traditional genetic counseling, and 13 (38.2%) had a family member with breast cancer but did not meet NCCN criteria. All patients opted to undergo genetic testing. Testing revealed six pathogenic variants in five patients (13.5%): CHEK2 (n = 2), MSH3 (n = 1), MUTYH (n = 1), and BRCA1 and HOXB13 (n = 1). No patients had a delay in time-to-treatment due to genetic testing turnaround time, nor did any patients undergo additional risk reducing surgery. There was no significant difference in median knowledge score between Chatbot and traditional counseling (11 vs. 12, p = 0.09) or in median patient satisfaction score (30 vs. 30, p = 0.19).

conclusionSatisfaction and comprehension in patients with breast cancer undergoing pre-test genetic counseling using an automated Chatbot is comparable to in-person genetic testing. Utilization of this technology can offer improved access to care and a much-needed alternative for pre-test counseling.

Indexed as

Breast NeoplasmsGenetic CounselingArtificial IntelligenceFemaleGenetic TestingHumansProspective StudiesArtificial intelligenceBreast cancerChatbotGenetic counselingGenetic testing

Identifiers

PMID37567976
OpenAlexW4385759496

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.