Evidence map›Paper›PMID 42557998›Full record

ArticleJournal of genetic counseling2026

An analysis of AI platforms: Can AI provide genetics education?

Ashley Shin, Riley Shin, Mia Ramirez, Ioana L Bondre, Brittanie S Shelton, Leslie Dunnington, Elizabeth K Nugent

Abstract read
In one paragraph

Article in Journal of genetic counseling, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Ashley ShinDivision of Plastic Surgery, Baylor Scott & White Medical Center-Baylor College of Medicine, Temple, Texas, USA.ORCID https://orcid.org/0000-0002-3757-9429
Riley ShinTexas Tech University Health Sciences Center School of Medicine, Lubbock, Texas, USA.ORCID https://orcid.org/0009-0004-3264-9119
Mia RamirezDepartment of Obstetrics & Gynecology, Tulane School of Medicine, New Orleans, Louisiana, USA.ORCID https://orcid.org/0009-0008-7814-7666
Ioana L BondreDivision of Gynecologic Oncology, Department of Obstetrics, Gynecology and Reproductive Sciences, McGovern Medical School at UTHealth Houston, Houston, Texas, USA.
Brittanie S SheltonDivision of Gynecologic Oncology, Department of Obstetrics, Gynecology and Reproductive Sciences, McGovern Medical School at UTHealth Houston, Houston, Texas, USA.
Leslie DunningtonDepartment of Pediatrics, McGovern Medical School at UTHealth Houston, Houston, Texas, USA.
Elizabeth K NugentDivision of Gynecologic Oncology, Department of Obstetrics, Gynecology and Reproductive Sciences, McGovern Medical School at UTHealth Houston, Houston, Texas, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the introduction, free access, and proliferation of various artificial intelligence (AI) in unique platforms, patients have access to more summarized information than ever. Our study aimed to explore the use of AI chatbots to improve patients' understanding of genetic testing. Thirty-nine frequently asked questions (FAQs) on genetic testing, including generic, BRCA-related, and Lynch syndrome-based questions, were asked to five AI chatbots (ChatGPT 3.5, Google Gemini, Reddit Answers, Bootcamp, and DeepSeek). Answers were individually assessed in a blind, randomized survey by two gynecologic oncologists and two genetic counselors on a 4-point scale (1: accurate and comprehensive, 2: accurate but inadequate, 3: incomplete, partially inaccurate, and/or outdated, 4: completely inaccurate) and statistically evaluated with ANOVA and Tukey testing. A Flesch-Kincaid Grade Level (FKGL) readability score and word count for each answer were obtained. Overall ratings show that no single AI platform outperforms the others in accuracy or readability, though Reddit Answers was notably worse. On average, ChatGPT 3.5, Google Gemini, Reddit Answers, Bootcamp, and DeepSeek received scores of 1.5, 1.6, 2.4, 1.6, and 1.4, respectively. The FKGL readability scores of answers across all AI platforms are consistently higher than the reading level of the average American (ChatGPT 3.5: 13.5, Google Gemini: 13.3, Reddit Answers: 12.1, Bootcamp: 14.6, DeepSeek: 14.2). Although AI chatbots have the potential to be a great resource for patients seeking genetic testing, especially for foundational genetic knowledge, this study highlights the importance of genetic counseling and education by healthcare professionals. Because genetic testing is unique to each patient, the answers produced by AI chatbots are not always applicable to each individual.

Indexed as

Artificial IntelligenceGenetic CounselingPatient Education as TopicGenetic TestingHumansartificial intelligencecounseling techniquesgenetic counselinggenetic literacygenetic testinghealth literacypatient education

Identifiers

PMID42557998
PMCPMC13444657

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.