Asset Details
Do you wish to reserve the book?
Exploring the use of large language models for classification, clinical interpretation, and treatment recommendation in breast tumor patient records
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Exploring the use of large language models for classification, clinical interpretation, and treatment recommendation in breast tumor patient records
Do you wish to request the book?
Exploring the use of large language models for classification, clinical interpretation, and treatment recommendation in breast tumor patient records
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Exploring the use of large language models for classification, clinical interpretation, and treatment recommendation in breast tumor patient records
2025
Overview
This study aims to investigate and compare the diagnostic performance, disease interpretation reliability, and treatment recommendation capabilities of multiple advanced large language models (GPT-4o, DeepSeek-R1, and DeepSeek-V3) in breast tumor cases. It retrospectively collected comprehensive clinical records of patients with breast tumors treated at Taizhou Cancer Hospital between January and April 2024. The study evaluated the accuracy of tumor classification (benign vs. malignant), the quality of disease interpretation, and the appropriateness of treatment recommendations generated by each model. To assess the clinical interpretability and utility of the models, a comprehensive performance analysis was conducted using statistical methods. A total of 45 patients with breast tumors were included, comprising 37 benign and 8 malignant cases (43 females, 2 males). GPT-4o achieved the highest area under the curve (AUC) for tumor classification (AUC = 0.848), outperforming DeepSeek-R1 (AUC = 0.736) and DeepSeek-V3 (AUC = 0.723). However, DeLong’s test indicated that the differences in AUCs among the models were not statistically significant (
p
> 0.05). In addition, subjective evaluations by doctors indicated that DeepSeek-R1 received the highest scores for disease interpretation (4.73 ± 0.46) and treatment recommendations (4.70 ± 0.51), with consistent ratings.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
