Using artificial intelligence to generate medical literature for urology patients: a comparison of three different large language models.
David PompiliYasmina RichaPatrick CollinsHelen RichardsDerek Barrry HennesseyPublished in: World journal of urology (2024)
While LLMs can generate PILs that may help reduce healthcare professional workload, generated content requires clinician input for accuracy and inclusion of health literacy aids, such as images. LLM-generated PILs were above the average reading level for adults, necessitating improvement in LLM algorithms and/or prompt design. How satisfied patients are to LLM-generated PILs remains to be evaluated.
Keyphrases
- artificial intelligence
- healthcare
- end stage renal disease
- machine learning
- newly diagnosed
- deep learning
- chronic kidney disease
- ejection fraction
- systematic review
- prognostic factors
- peritoneal dialysis
- autism spectrum disorder
- big data
- patient reported outcomes
- convolutional neural network
- patient reported
- health insurance