Abstract 4370282: The Latest Large Language Model, Grok: Can It Provide Education about Atrial Fibrillation for Diverse Populations?

Background: Large language models (LLMs) are used by atrial fibrillation patients. ChatGPT (OpenAI, San Francisco) and Grok (X.ai, San Francisco) have 450 M, 35 M monthly users, respectively. Grok is the newest LLM, open-sourced, uses Mixture of Experts algorithms, has 314 billion parameters, known for STEM answers and the use of X (formerly Twitter) as a data source. Grok was meant to be conversational in tone. LLMS are trained by data sets initially trained by software engineers and later by AI in part or exclusively. It is not known whether Grok responses about atrial fibrillation queries differ by patient gender and race/ethnicity. Methods: We used the query: “I am a 68-year-old [ethnic/racial group] [male/female] with atrial fibrillation. I had a heart attack 2 years ago with stents. What can I expect from my cardiologist?” Three ethnic groups (White, African American, and Latinx) and male/female gender. Response analysis: Word Count (WC) and Flesch-Kincaid Grade Level (FK). ChatGPT4.5 reviewed the LLM responses for cultural sensitivity. Results: Average WC: ChatGPT= 312.5±110.5, Grok= 830.7±104.7. Average FK: ChatGPT=10.7±0.9, Grok=10.3±1.0. Grok showed high cultural sensitivity, for African American female and Latinx users, e.g. diet, cardiovascular risk factors. Both male and female prompts were treated equitably in tone, depth, and scope. However, Grok did not incorporate culturally relevant content for White male or female users. For the Hispanic prompt, Grok mentioned the existence of “language services” but no website links or related organizations for further help. CHA2DS2-VASc is mentioned by both ChatGPT and Grok. Grok has a lower reading grade level for White males, Black females, Hispanic males than that of ChatGPT which may reflect their use of X (formerly Twitter) data. Grok had the longest response for Black females versus all other ethnic groups in this small study. Conclusion: Grok, the latest LLM, competes well with ChatGPT with its thoroughness and factual medical education answers. Reading level however varies by racial/ethnic group and gender.

Authors

Institutions

Publication Details

Journal
Circulation
Published
2025-11-03
DOI
https://doi.org/10.1161/circ.152.suppl_3.4370282
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract 4370282: The Latest Large Language Model, Grok: Can It Provide Education about Atrial Fibrillation for Diverse Populations?

Margaret Wang, Paul J. Wang, Ngoc Bich Hoang, Eunah Chung et al.
Circulation
Artificial Intelligence in Healthcare and Education
article

Abstract 4370282: The Latest Large Language Model, Grok: Can It Provide Education about Atrial Fibrillation for Diverse Populations?

Margaret Wang, Paul J. Wang, Ngoc Bich Hoang, Eunah Chung, Gloria Wu, Owais Khan, N. Mendoza, Hrishi Paliath-Pathiyal, Riki Toram, Viki Toram, Ivan Chim
article en

Abstract

Background: Large language models (LLMs) are used by atrial fibrillation patients. ChatGPT (OpenAI, San Francisco) and Grok (X.ai, San Francisco) have 450 M, 35 M monthly users, respectively. Grok is the newest LLM, open-sourced, uses Mixture of Experts algorithms, has 314 billion parameters, known for STEM answers and the use of X (formerly Twitter) as a data source. Grok was meant to be conversational in tone. LLMS are trained by data sets initially trained by software engineers and later by AI in part or exclusively. It is not known whether Grok responses about atrial fibrillation queries differ by patient gender and race/ethnicity. Methods: We used the query: “I am a 68-year-old [ethnic/racial group] [male/female] with atrial fibrillation. I had a heart attack 2 years ago with stents. What can I expect from my cardiologist?” Three ethnic groups (White, African American, and Latinx) and male/female gender. Response analysis: Word Count (WC) and Flesch-Kincaid Grade Level (FK). ChatGPT4.5 reviewed the LLM responses for cultural sensitivity. Results: Average WC: ChatGPT= 312.5±110.5, Grok= 830.7±104.7. Average FK: ChatGPT=10.7±0.9, Grok=10.3±1.0. Grok showed high cultural sensitivity, for African American female and Latinx users, e.g. diet, cardiovascular risk factors. Both male and female prompts were treated equitably in tone, depth, and scope. However, Grok did not incorporate culturally relevant content for White male or female users. For the Hispanic prompt, Grok mentioned the existence of “language services” but no website links or related organizations for further help. CHA2DS2-VASc is mentioned by both ChatGPT and Grok. Grok has a lower reading grade level for White males, Black females, Hispanic males than that of ChatGPT which may reflect their use of X (formerly Twitter) data. Grok had the longest response for Black females versus all other ethnic groups in this small study. Conclusion: Grok, the latest LLM, competes well with ChatGPT with its thoroughness and factual medical education answers. Reading level however varies by racial/ethnic group and gender.

CirculationVol. 152(Suppl_3)
Boston University (US), Santa Clara University (US), Palo Alto University (US), Nova Southeastern University (US), University of California San Diego (US), Universidad para la Cooperación Internacional (CR), Clovis Oncology (United States) (US), San Francisco State University (US), University of California, Davis (US), Stanford University (US)
Openalex Percentile: Top 75%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.