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
- Daniel Flores (ORCID: https://orcid.org/0009-0004-4853-632X)
- Faris F Halaseh
- Ariana M. Nelson (ORCID: https://orcid.org/0000-0003-1575-1635)
- Saman Andalib (ORCID: https://orcid.org/0009-0002-3281-8256)
- Aidin Spina (ORCID: https://orcid.org/0000-0003-3994-9646)
- Rishi Vermani (ORCID: https://orcid.org/0009-0001-9417-7645)
Institutions
- University of California, Irvine (US)
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