Impact of explainable artificial intelligence assistance on clinical decision making of novice dental clinicians

Objective: Despite artificial intelligence (AI) is being used increasingly in healthcare, implementation challenges exist leading to potential biases during the clinical decision process of the practitioner. The interaction of AI with a novice clinician was investigated through an identification task, an important component of diagnosis, in dental radiography. The study evaluated the performance, efficiency, and confidence level of dental students on radiographic identification of FI, with and without AI assistance.
Materials and Methods: 22 third- and 19 fourth-year dental students (DS3 and DS4, respectively) completed remotely administered surveys to identify FI lesions on a series of dental radiographs. The control group received radiographs without AI assistance while the test group received the same radiographs and AI-labeled radiographs. Data was appropriately analyzed using the Chi-square, Fischer’s exact, ANOVA or Kruskal-Wallis tests. 
Results: Performance between groups with a..., , , # Data from: Impact of explainable artificial intelligence assistance on clinical decision making of novice dental clinicians

[https://doi.org/10.5061/dryad.j0zpc86h6](https://doi.org/10.5061/dryad.j0zpc86h6)

## Description of the data and file structure

#### File: DryadData.csv

**Description:** Data category description presented in row 2. All time in seconds.

研究目标：尽管人工智能（Artificial Intelligence, AI）在医疗领域的应用日益广泛，但仍存在落地挑战，可能在临床从业者的临床决策过程中引入潜在偏倚。本研究针对牙科放射学中的识别任务（诊断的重要组成部分），探究了人工智能与新手临床医师的交互模式。研究评估了牙科学生在有、无人工智能辅助下，对牙科放射影像中FI病灶的识别效能、效率及自信水平。

材料与方法：共招募22名三年级牙科学生（DS3）与19名四年级牙科学生（DS4），通过远程调研完成一系列牙科放射影像中的FI病灶识别任务。对照组仅接收原始放射影像，无人工智能辅助；实验组则接收相同的原始放射影像与人工智能标注后的影像。研究采用卡方检验、费希尔精确检验、方差分析（Analysis of Variance, ANOVA）及克鲁斯卡尔-沃利斯检验进行恰当的数据分析。

结果：两组间的效能……
# 数据来源：《可解释人工智能辅助对新手牙科临床医师临床决策的影响》
[https://doi.org/10.5061/dryad.j0zpc86h6]

## 数据与文件结构说明
#### 文件：DryadData.csv
**描述：** 第2行展示数据类别说明。所有时间数据的单位均为秒。