聊天机器人特征
包含27个聊天机器人对精神病学执照考试问题的回答，用于评估其临床知识和推理能力。
基本信息
资源简介
聊天机器人特征
【数据集背景】
本数据集源站标题为《Chatbot characteristics.》。源站首次发布：2025-08-14。
【数据内容】
源站原始描述：Chatbots are increasingly being recognized as valuable tools for clinical support in psychiatry. This study systematically evaluated the clinical knowledge and reasoning of 27 leading chatbots in psychiatry. Using 160 multiple-choice questions from the Taiwan Psychiatry Licensing Examinations and Rasch analysis, we quantified performance and qualitatively assessed reasoning processes. OpenAI’s ChatGPT-o1-preview emerged as the top performer, achieving a Rasch ability score of 2.23, significantly…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 2.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0330303.t002。
【主题与分类】
源站学科分类：Cell Biology、Biotechnology、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Developmental Biology、Cancer。主题标签：using 160 multiple、study systematically evaluated、rigorous human oversight、openai &# 8217、current limitations underscore、xlink "> chatbots、27 leading chatbots、occasional reasoning biases、rasch ability score、reasoning </ p。
【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://plos.figshare.com/articles/dataset/Chatbot_characteristics_/29914848
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Chang, Yu; Huang, Si-Sheng; Hsu, Wen-Yu; Liu, Yi-Chun (2025). Chatbot characteristics.. PLOS ONE. Dataset
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：Chatbot characteristics.
- DOI：10.1371/journal.pone.0330303.t002
- 发布日期：2025-08-14
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Chatbot_characteristics_/29914848
命令行下载
curl -L -o "Table 2.xls" "https://ndownloader.figshare.com/files/57196758"
文件清单
Table 2.xls（5.63 KB）
文件总体积 5.63 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
Chatbots are increasingly being recognized as valuable tools for clinical support in psychiatry. This study systematically evaluated the clinical knowledge and reasoning of 27 leading chatbots in psychiatry. Using 160 multiple-choice questions from the Taiwan Psychiatry Licensing Examinations and Rasch analysis, we quantified performance and qualitatively assessed reasoning processes. OpenAI’s ChatGPT-o1-preview emerged as the top performer, achieving a Rasch ability score of 2.23, significantly surpassing the passing threshold (p < 0.001). While it excelled in diagnostic and therapeutic reasoning, it also demonstrated notable limitations in factual recall, niche topics, and occasional reasoning biases. Our findings indicate that while advanced chatbots hold significant potential as clinical decision-support tools, their current limitations underscore that rigorous human oversight is indispensable for patient safety. Continuous evaluation and domain-specific training are crucial for the safe integration of these technologies into clinical practice.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0330303.t002（source: https://plos.figshare.com/articles/dataset/Chatbot_characteristics_/29914848）。
需要用于商业化训练或医疗器械注册（NMPA/FDA）？
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