Evidence map›Paper›PMID 37934933›Full record

SynthesisJCO clinical cancer informatics2023

Chatbot Artificial Intelligence for Genetic Cancer Risk Assessment and Counseling: A Systematic Review and Meta-Analysis.

Emily M Webster, Muhammad Danyal Ahsan, Luiza Perez, Sarah R Levi, Charlene Thomas, Paul Christos, Andy Hickner, Jada G Hamilton, Kemi Babagbemi, Evelyn Cantillo and 4 more

Erratum issuedOpen access · greenAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in JCO clinical cancer informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
10.6field-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

26 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors at 2 institutions in 1 country.

Emily M WebsterWeill Cornell Medicine, New York, NY.ORCID 0000-0002-3208-4320
Muhammad Danyal AhsanWeill Cornell Medicine, New York, NY.ORCID 0000-0001-8633-8931
Luiza PerezWeill Cornell Medicine, New York, NY.ORCID 0000-0002-3710-380X
Sarah R LeviWeill Cornell Medicine, New York, NY.
Charlene ThomasWeill Cornell Medicine, New York, NY.ORCID 0000-0002-7685-0090
Paul ChristosWeill Cornell Medicine, New York, NY.
Andy HicknerWeill Cornell Medicine, New York, NY.ORCID 0000-0003-1922-3813
Jada G HamiltonMemorial Sloan Kettering Cancer Center, New York, NY.ORCID 0000-0001-8377-4666
Kemi BabagbemiWeill Cornell Medicine, New York, NY.
Evelyn CantilloWeill Cornell Medicine, New York, NY.ORCID 0000-0001-6269-8748
Kevin HolcombWeill Cornell Medicine, New York, NY.ORCID 0000-0002-6916-5271
Eloise Chapman-DavisWeill Cornell Medicine, New York, NY.
Ravi N SharafWeill Cornell Medicine, New York, NY.ORCID 0000-0002-6905-9823
Melissa K FreyWeill Cornell Medicine, New York, NY.ORCID 0000-0002-6705-1211
Cornell University · USMemorial Sloan Kettering Cancer Center · US

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI SELWYN M VICKERS · 1985 to 2026
$347.4M
Clinical and Translational Science AwardUL1TR001873 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI REILLY, MUREDACH P · 2016 to 2025
$99.0M
Disparities in COVID Disease Severity and Outcomes in New York CityUL1TR002384 · NCATS · WEILL MEDICAL COLL OF CORNELL UNIV · PI JULIANNE L IMPERATO-MCGINLEY · 2017 to 2026
$86.2M
Implementing Universal Lynch Syndrome Screening across Multiple Healthcare Systems: Identifying Strategies to Facilitate and Maintain Programs in Different Organizational ContextsR01CA211723 · NCI · GEISINGER CLINIC · PI RAHM, ALANNA K · 2017 to 2021
$3.7M
Gene Dependent Cancer Risk and Optimal Cancer Prevention Strategies in Lynch SyndromeK07CA216326 · NCI · WEILL MEDICAL COLL OF CORNELL UNIV · PI SHARAF, RAVI · 2018 to 2021
$675k
NCATS NIH HHS UL1 TR001873NCATS NIH HHS UL1 TR002384NCI NIH HHS K07 CA216326NCI NIH HHS P30 CA008748NCI NIH HHS R01 CA211723
6 · The paper itself

Abstract

purposeMost individuals with a hereditary cancer syndrome are unaware of their genetic status to underutilization of hereditary cancer risk assessment. Chatbots, or programs that use artificial intelligence to simulate conversation, have emerged as a promising tool in health care and, more recently, as a potential tool for genetic cancer risk assessment and counseling. Here, we evaluated the existing literature on the use of chatbots in genetic cancer risk assessment and counseling.

methodsA systematic review was conducted using key electronic databases to identify studies which use chatbots for genetic cancer risk assessment and counseling. Eligible studies were further subjected to meta-analysis.

resultsSeven studies met inclusion criteria, evaluating five distinct chatbots. Three studies evaluated a chatbot that could perform genetic cancer risk assessment, one study evaluated a chatbot that offered patient counseling, and three studies included both functions. The pooled estimated completion rate for the genetic cancer risk assessment was 36.7% (95% CI, 14.8 to 65.9). Two studies included comprehensive patient characteristics, and none involved a comparison group. Chatbots varied as to the involvement of a health care provider in the process of risk assessment and counseling.

conclusionChatbots have been used to streamline genetic cancer risk assessment and counseling and hold promise for reducing barriers to genetic services. Data regarding user and nonuser characteristics are lacking, as are data regarding comparative effectiveness to usual care. Future research may consider the impact of chatbots on equitable access to genetic services.

Indexed as

Artificial IntelligenceNeoplastic Syndromes, HereditaryCounselingHumansRisk AssessmentSoftware

Identifiers

PMID37934933
PMCPMC10730073
OpenAlexW4388467132

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.