骨癌问答LLM准确性评估表

数据集获取（Figshare）

• 源站标题：Table 2_Evaluation of the accuracy of large language models in answering bone cancer-related questions.xlsx
• DOI：10.3389/fpubh.2025.1719047.s001
• 发布日期：2025-12-05
• 文件数：1 个
• 文件总体积：12.86 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_2_Evaluation_of_the_accuracy_of_large_language_models_in_answering_bone_cancer-related_questions_xlsx/30797480

命令行下载

curl -L -o "Table 2_Evaluation of the accuracy of large language models in answering bone cancer-related questions.xlsx" "https://ndownloader.figshare.com/files/60121313"

文件清单

• Table 2_Evaluation of the accuracy of large language models in answering bone cancer-related questions.xlsx（12.86 KB）

文件总体积 12.86 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

IntroductionLarge Language Models (LLMs) excel at understanding medical terminology, parsing unstructured clinical data, and generating contextually relevant insights, emerging as transformative healthcare tools. Three leading LLMs—Deepseek, ChatGPT, and Grok—show great potential for medical education, clinical decision-making, and patient care. Bone cancer includes diverse primary and metastatic tumors, each with distinct diagnostic criteria, treatment pathways, and prognoses. Based on this guideline, this study assesses the accuracy of Deepseek V3.1, ChatGPT 5, and Grok 4 in addressing bone cancer-related questions.
MethodsBased on the clinical guidelines for bone cancer released by the NCCN in April 2025, 52 questions related to bone cancer were developed. Researchers posed questions to Deepseek V3.1, ChatGPT 5, and Grok 4, and collected the data generated; each LLM was queried twice within a one-month period. The collected data were independently evaluated and scored by two bone cancer-treating specialists in accordance with the scoring criteria.
ResultsAmong the answers to the 52 bone cancer-related questions, the probability of Deepseek V3.1, ChatGPT 5, and Grok 4 providing correct responses in both rounds was greater than 90%. Additionally, no correlation was observed between the LLMs’ scores, word count, and response times. The total scores of Deepseek V3.1, ChatGPT 5, and Grok 4 were 3.75 ± 0.71, 3.81 ± 0.6, and 0.87 ± 0.51, respectively. The word count of responses from Deepseek V3.1, ChatGPT 5, and Grok 4 was 546.56 ± 194.49, 367.02 ± 273.18, and 194.16 ± 197.07 words, respectively. The response times of Deepseek V3.1, ChatGPT 5, and Grok 4 were 11.83 ± 3.41 s, 1.52 ± 0.52 s and 42.48 ± 26.89 s, respectively. No statistically significant differences in scores were found for any of the LLMs between the two rounds. However, ChatGPT 5 showed a statistically significant difference in word count between the two rounds (360.12 ± 279.89 vs. 373.94 ± 268.86 words).
ConclusionWhen answering bone cancer-related questions, Deepseek V3.1, ChatGPT 5, and Grok 4 generally performed well. Specifically, when responding to questions about Ewing sarcoma, ChatGPT 5 and

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fpubh.2025.1719047.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_2_Evaluation_of_the_accuracy_of_large_language_models_in_answering_bone_cancer-related_questions_xlsx/30797480）。