DataSheet_1_ChatGPT-4在放射肿瘤学培训考试和Red Journal Gray Zone病例上的基准测试：AI辅助放射肿瘤学医学教育与决策的潜力与挑战.zip
评估ChatGPT-4在放射肿瘤学培训考试和Red Journal Gray Zone病例中表现的数据集。
基本信息
资源简介
DataSheet_1_ChatGPT-4在放射肿瘤学培训考试和Red Journal Gray Zone病例上的基准测试：AI辅助放射肿瘤学医学教育与决策的潜力与挑战.zip
【一句话概览】
本数据集是 figshare 公开的文本数据集，收录 1 个文件、合计 2.57 MB，主题涵盖 large language model、radiotherapy、natural language processing、artificial intelligence、Gray Zone，可用于医学自然语言处理（实体识别、信息抽取等），CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《DataSheet_1_Benchmarking ChatGPT-4 on a radiation oncology in-training exam and Red Journal Gray Zone cases: potentials and challenges for ai-assisted medical education and decision making in radiation oncology.zip》。配套论文《Benchmarking ChatGPT-4 on a radiation oncology in-training exam and Red Journal Gray Zone cases: potentials and challenges for ai-assisted medical education and decision making in radiation oncology》发表于 Frontiers in Oncology（2023），DOI：10.3389/fonc.2023.1265024。源站首次发布：2023-09-14。
【数据内容】
源站原始描述：PurposeThe potential of large language models in medicine for education and decision-making purposes has been demonstrated as they have achieved decent scores on medical exams such as the United States Medical Licensing Exam (USMLE) and the MedQA exam. This work aims to evaluate the performance of ChatGPT-4 in the specialized field of radiation oncology. MethodsThe 38th American College of Radiology (ACR) radiation oncology in-training (TXIT) exam and the 2022 Red Journal Gray Zone cases are use…
【数据结构与技术规格】
文件数 1 个，合计 2.57 MB。DataSheet_1_Benchmarking ChatGPT-4 on a radiation oncology in-training exam and Red Journal Gray Zone cases: potentials and challenges for ai-assisted.zip：application/zip，2.57 MB。数据集 DOI：10.3389/fonc.2023.1265024.s001。
【主题与分类】
源站学科分类：Cancer、Haematological Tumours、Molecular Targets、Radiation Therapy、Oncology and Carcinogenesis not elsewhere classified、Solid Tumours。主题标签：large language model、radiotherapy、natural language processing、artificial intelligence、Gray Zone、clinical decision support (CDS)。
【适用方向】
文本类数据可用于医学自然语言处理，如命名实体识别、信息抽取与文本分类。
【获取与许可】
源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet_1_Benchmarking_ChatGPT-4_on_a_radiation_oncology_in-training_exam_and_Red_Journal_Gray_Zone_cases_potentials_and_challenges_for_ai-assisted_medical_education_and_decision_making_in_radiation_oncology_zip/24136914
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Huang, Yixing; Gomaa, Ahmed; Semrau, Sabine; Haderlein, Marlen; Lettmaier, Sebastian; Weissmann, Thomas; et al. (2023). DataSheet_1_Benchmarking ChatGPT-4 on a radiation oncology in-training exam and Red Journal Gray Zone cases: potentials and challenges for ai-assisted medical education and decisio…
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：DataSheet_1_Benchmarking ChatGPT-4 on a radiation oncology in-training exam and Red Journal Gray Zone cases: potentials and challenges for ai-assisted medical education and decision making in radiation oncology.zip
- DOI：10.3389/fonc.2023.1265024.s001
- 发布日期：2023-09-14
- 文件数：1 个
- 文件总体积：2.57 MB
- 源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet_1_Benchmarking_ChatGPT-4_on_a_radiation_oncology_in-training_exam_and_Red_Journal_Gray_Zone_cases_potentials_and_challenges_for_ai-assisted_medical_education_and_decision_making_in_radiation_oncology_zip/24136914
命令行下载
curl -L -o "DataSheet_1_Benchmarking ChatGPT-4 on a radiation oncology in-training exam and Red Journal Gray Zone cases: potentials and challenges for ai-assisted.zip" "https://ndownloader.figshare.com/files/42345087"
文件清单
DataSheet_1_Benchmarking ChatGPT-4 on a radiation oncology in-training exam and Red Journal Gray Zone cases: potentials and challenges for ai-assisted.zip（2.57 MB）
文件总体积 2.57 MB（源站 API 实测）。
数据说明
PurposeThe potential of large language models in medicine for education and decision-making purposes has been demonstrated as they have achieved decent scores on medical exams such as the United States Medical Licensing Exam (USMLE) and the MedQA exam. This work aims to evaluate the performance of ChatGPT-4 in the specialized field of radiation oncology.
MethodsThe 38th American College of Radiology (ACR) radiation oncology in-training (TXIT) exam and the 2022 Red Journal Gray Zone cases are used to benchmark the performance of ChatGPT-4. The TXIT exam contains 300 questions covering various topics of radiation oncology. The 2022 Gray Zone collection contains 15 complex clinical cases.
ResultsFor the TXIT exam, ChatGPT-3.5 and ChatGPT-4 have achieved the scores of 62.05% and 78.77%, respectively, highlighting the advantage of the latest ChatGPT-4 model. Based on the TXIT exam, ChatGPT-4’s strong and weak areas in radiation oncology are identified to some extent. Specifically, ChatGPT-4 demonstrates better knowledge of statistics, CNS & eye, pediatrics, biology, and physics than knowledge of bone & soft tissue and gynecology, as per the ACR knowledge domain. Regarding clinical care paths, ChatGPT-4 performs better in diagnosis, prognosis, and toxicity than brachytherapy and dosimetry. It lacks proficiency in in-depth details of clinical trials. For the Gray Zone cases, ChatGPT-4 is able to suggest a personalized treatment approach to each case with high correctness and comprehensiveness. Importantly, it provides novel treatment aspects for many cases, which are not suggested by any human experts.
ConclusionBoth evaluations demonstrate the potential of ChatGPT-4 in medical education for the general public and cancer patients, as well as the potential to aid clinical decision-making, while acknowledging its limitations in certain domains. Owing to the risk of hallucinations, it is essential to verify the content generated by models such as ChatGPT for accuracy.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.3389/fonc.2023.1265024.s001（source: https://frontiersin.figshare.com/articles/dataset/DataSheet_1_Benchmarking_ChatGPT-4_on_a_radiation_oncology_in-training_exam_and_Red_Journal_Gray_Zone_cases_potentials_and_challenges_for_ai-assisted_medical_education_and_decision_making_in_radiation_oncology_zip/24136914）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
⚡️ 需要数据支持或标注服务？立即联系我们获取专业方案。
