Evaluation of Generative Language Models in Personalizing Medical Information: Instrument Validation Study

GLMs can change the structure and readability of medical text outputs according to input-specified education. However, GLMs categorize input education designation into 3 broad tiers of output readability: easy (6th and 8th grade), medium (high school), and difficult (bachelor's degree). This is the first result to suggest that there are broader boundaries in the success of GLMs in output text simplification. Future research must establish how GLMs can reliably personalize medical texts to prespecified education levels to enable a broader impact on health care literacy.

Authors

Institutions

Publication Details

Journal
JMIR AI
Published
2024-08-13
DOI
https://doi.org/10.2196/54371
Citations
8
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.96
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Evaluation of Generative Language Models in Personalizing Medical Information: Instrument Validation Study

Daniel Flores, Faris F Halaseh, Ariana M. Nelson, Saman Andalib et al.
8 citations
JMIR AI
Artificial Intelligence in Healthcare and Education
0.96
article

Evaluation of Generative Language Models in Personalizing Medical Information: Instrument Validation Study

Daniel Flores, Faris F Halaseh, Ariana M. Nelson, Saman Andalib, Aidin Spina, Rishi Vermani
article en
8 citations

Abstract

GLMs can change the structure and readability of medical text outputs according to input-specified education. However, GLMs categorize input education designation into 3 broad tiers of output readability: easy (6th and 8th grade), medium (high school), and difficult (bachelor's degree). This is the first result to suggest that there are broader boundaries in the success of GLMs in output text simplification. Future research must establish how GLMs can reliably personalize medical texts to prespecified education levels to enable a broader impact on health care literacy.

JMIR AIVol. 3
University of California, Irvine (US)
Quality Education
Openalex Percentile: Top 22%
Artificial Intelligence in Healthcare and Education
0.96
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.

Evaluation of Generative Language Models in Personalizing Medical Information: Instrument Validation Study — Daniel Flores, Faris F Halaseh, et al. · JMIR AI (2024) | TGRS Research Map | TGRS