This study evaluates the efficacy of GPT-4, a Large Language Model, in simplifying medical literature for enhancing patient comprehension in glaucoma care. GPT-4 was used to transform published abstracts from 3 glaucoma journals (n = 62) and patient education materials (Patient Educational Model [PEMs], n = 9) to a 5th-grade reading level. GPT-4 was also prompted to generate de novo educational outputs at 6 different education levels (5th Grade, 8th Grade, High School, Associate's, Bachelor's and Doctorate). Readability of both transformed and de novo materials was quantified using Flesch Kincaid Grade Level (FKGL) and Flesch Reading Ease (FKRE) Score. Latent semantic analysis (LSA) using cosine similarity was applied to assess content consistency in transformed materials. The transformation of abstracts resulted in FKGL decreasing by an average of 3.21 points (30%, P < .001) and FKRE increasing by 28.6 points (66%, P < .001). For PEMs, FKGL decreased by 2.38 points (28%, P = .0272) and FKRE increased by 12.14 points (19%, P = .0459). LSA revealed high semantic consistency, with an average cosine similarity of 0.861 across all abstracts and 0.937 for PEMs, signifying topical themes were quantitatively shown to be consistent. This study shows that GPT-4 effectively simplifies medical information about glaucoma, making it more accessible while maintaining textual content. The improved readability scores for both transformed materials and GPT-4 generated content demonstrate its usefulness in patient education across different educational levels.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1097/MD.0000000000041059 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Learning the language of antibody hypervariability.
Proc Natl Acad Sci U S A
January 2025
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139.
Protein language models (PLMs) have demonstrated impressive success in modeling proteins. However, general-purpose "foundational" PLMs have limited performance in modeling antibodies due to the latter's hypervariable regions, which do not conform to the evolutionary conservation principles that such models rely on. In this study, we propose a transfer learning framework called Antibody Mutagenesis-Augmented Processing (AbMAP), which fine-tunes foundational models for antibody-sequence inputs by supervising on antibody structure and binding specificity examples.
View Article and Find Full Text PDFQwen-2.5 Outperforms Other Large Language Models in the Chinese National Nursing Licensing Examination: Retrospective Cross-Sectional Comparative Study.
JMIR Med Inform
January 2025
Department of Science and Education, Shenzhen Baoan Women's and Children's Hospital, Shenzhen, China.
Background: Large language models (LLMs) have been proposed as valuable tools in medical education and practice. The Chinese National Nursing Licensing Examination (CNNLE) presents unique challenges for LLMs due to its requirement for both deep domain-specific nursing knowledge and the ability to make complex clinical decisions, which differentiates it from more general medical examinations. However, their potential application in the CNNLE remains unexplored.
View Article and Find Full Text PDFEvaluating ChatGPT's Efficacy in Pediatric Pneumonia Detection From Chest X-Rays: Comparative Analysis of Specialized AI Models.
JMIR AI
January 2025
Department of Radiology, Children's National Hospital, Washington, DC, United States.
The role of chromatin state in intron retention: A case study in leveraging large scale deep learning models.
PLoS Comput Biol
January 2025
Department of Computer Science, Colorado State University, Fort Collins, Colorado, United States of America.
Complex deep learning models trained on very large datasets have become key enabling tools for current research in natural language processing and computer vision. By providing pre-trained models that can be fine-tuned for specific applications, they enable researchers to create accurate models with minimal effort and computational resources. Large scale genomics deep learning models come in two flavors: the first are large language models of DNA sequences trained in a self-supervised fashion, similar to the corresponding natural language models; the second are supervised learning models that leverage large scale genomics datasets from ENCODE and other sources.
View Article and Find Full Text PDFEnhancing semantical text understanding with fine-tuned large language models: A case study on Quora Question Pair duplicate identification.
PLoS One
January 2025
Department of Biomedical and Health Informatics, Tsui Laboratory, Children's Hospital of Philadelphia, Philadelphia, PA, United States of America.
Semantical text understanding holds significant importance in natural language processing (NLP). Numerous datasets, such as Quora Question Pairs (QQP), have been devised for this purpose. In our previous study, we developed a Siamese Convolutional Neural Network (S-CNN) that achieved an F1 score of 82.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!