Utility of Large Language Models for Health Care Professionals and Patients in Navigating Hematopoietic Stem Cell Transplantation: Comparison of the Performance of ChatGPT-3.5, ChatGPT-4, and Bard.
Elisabetta XueDara Bracken-ClarkeGiovanni Maria IannantuonoHyoyoung Choo-WosobaJames L GulleyCharalampos S FloudasPublished in: Journal of medical Internet research (2024)
In conclusion, despite LLMs' potential capability in confronting challenging medical topics such as HSCT, the presence of mistakes and lack of clear references make them not yet appropriate for routine, unsupervised clinical use, or patient counseling. Implementation of LLMs' ability to access and to reference current and updated websites and research papers, as well as development of LLMs trained in specialized domain knowledge data sets, may offer potential solutions for their future clinical application.
Keyphrases
- healthcare
- end stage renal disease
- ejection fraction
- newly diagnosed
- chronic kidney disease
- primary care
- machine learning
- acute myeloid leukemia
- peritoneal dialysis
- prognostic factors
- palliative care
- autism spectrum disorder
- clinical practice
- human health
- smoking cessation
- resistance training
- artificial intelligence
- big data
- electronic health record
- patient reported outcomes
- antiretroviral therapy
- patient reported