Abstract 4116844: Simplifying Cardiology Research Abstracts: Assessing ChatGPT's Readability and Comprehensibility for Non-Medical Audiences

Background: Artificial Intelligence (AI)-powered chatbots like ChatGPT are increasingly used in academic medical settings to help with tasks such as evidence synthesis and manuscript drafting. They have shown potential in simplifying complex medical texts for non-medical audiences like patients and journalists. However, less is known about whether simplified texts may exclude important information or be of interest to patients or other non-medically-trained people such as journalists. Objective: This study aims to assess ChatGPT's capacity to simplify cardiology research abstracts by eliminating jargon and enhancing universal comprehension. Methods: We analyzed all abstracts and scientific statements published from July to November 2023 in Circulation (n=113). These abstracts were processed through ChatGPT with the prompt: "Please rewrite the following text to be comprehensible at a 5th-grade reading level. Retain all original information and exclude nothing". We assessed the readability of both original and simplified texts using Flesch-Kincaid Grade Level (FKGL) and Reading Ease (FKRE) scores. Additionally, a panel of five physicians and five laypeople evaluated these texts for completeness, accuracy, and understandability. Results: ChatGPT transformation or abstracts reduced the required reading level from a college graduate to 8-9th grade by both FKGL (18.3 to 8.6; p<.001) and FKRE (14.6 to 68.5; p<.001). The physician panel generally found that the simplified abstracts retained all essential information without introducing errors. Both physicians and laypeople found the simplified texts more comprehensible. Laypeople appreciated the ease of understanding but did not strongly express a desire for more such simplified texts, and some still worried about the simplified abstracts missing important details. Conclusion: ChatGPT simplifies medical research abstracts to a level understandable by those with less than a high school education without excluding important information. This approach may be useful to communicate new research findings to patients and other non-medically-trained people.

Authors

Institutions

Publication Details

Journal
Circulation
Published
2024-11-12
DOI
https://doi.org/10.1161/circ.150.suppl_1.4116844
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 4116844: Simplifying Cardiology Research Abstracts: Assessing ChatGPT's Readability and Comprehensibility for Non-Medical Audiences

Zachary Falk, Ruina Zhang, Vinay Kini, Kabir Malkani et al.
Circulation
Artificial Intelligence in Healthcare and Education
article

Abstract 4116844: Simplifying Cardiology Research Abstracts: Assessing ChatGPT's Readability and Comprehensibility for Non-Medical Audiences

Zachary Falk, Ruina Zhang, Vinay Kini, Kabir Malkani, Ryan Hughes, Alexander Zhao, Prianca Tawde, Griffin Collins, Danielle Maizes, Melissa M. Parker
article en

Abstract

Background: Artificial Intelligence (AI)-powered chatbots like ChatGPT are increasingly used in academic medical settings to help with tasks such as evidence synthesis and manuscript drafting. They have shown potential in simplifying complex medical texts for non-medical audiences like patients and journalists. However, less is known about whether simplified texts may exclude important information or be of interest to patients or other non-medically-trained people such as journalists. Objective: This study aims to assess ChatGPT's capacity to simplify cardiology research abstracts by eliminating jargon and enhancing universal comprehension. Methods: We analyzed all abstracts and scientific statements published from July to November 2023 in Circulation (n=113). These abstracts were processed through ChatGPT with the prompt: "Please rewrite the following text to be comprehensible at a 5th-grade reading level. Retain all original information and exclude nothing". We assessed the readability of both original and simplified texts using Flesch-Kincaid Grade Level (FKGL) and Reading Ease (FKRE) scores. Additionally, a panel of five physicians and five laypeople evaluated these texts for completeness, accuracy, and understandability. Results: ChatGPT transformation or abstracts reduced the required reading level from a college graduate to 8-9th grade by both FKGL (18.3 to 8.6; p<.001) and FKRE (14.6 to 68.5; p<.001). The physician panel generally found that the simplified abstracts retained all essential information without introducing errors. Both physicians and laypeople found the simplified texts more comprehensible. Laypeople appreciated the ease of understanding but did not strongly express a desire for more such simplified texts, and some still worried about the simplified abstracts missing important details. Conclusion: ChatGPT simplifies medical research abstracts to a level understandable by those with less than a high school education without excluding important information. This approach may be useful to communicate new research findings to patients and other non-medically-trained people.

CirculationVol. 150(Suppl_1)
Cornell University (US), Presbyterian Hospital (US)
Quality Education
Openalex Percentile: Top 85%
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.