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Related Concept Videos

What is an Experiment?01:12

What is an Experiment?

An experiment is a planned activity carried out under controlled conditions. The purpose of an experiment is to investigate the relationship between two variables. When one variable causes change in another, we call the first variable the explanatory or independent variable. The affected variable is called the response or dependent variable. In a randomized experiment, the researcher manipulates values of the explanatory variable and measures the resulting changes in the response variable. The...
Blinding01:11

Blinding

Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.
Cochran's Q Test01:17

Cochran's Q Test

Cochran's Q Test is a nonparametric statistical test used to determine if there are potential differences in the outcomes of three or more related groups on a binary (yes/no) or dichotomous outcome. It is essentially an extension of the McNemar Test, which is limited to two related samples - Cochran's Q test can handle three or more related samples, making it more versatile in scenarios where subjects are measured under multiple conditions. The test statistic follows a Chi-Square distribution,...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Study Designs in Epidemiology01:20

Study Designs in Epidemiology

Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and case-control studies.

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Related Experiment Videos

Exploring tuberculosis physicians' preferences for AI explainability in China: a protocol for a discrete choice

Jiale Zhang1, Qian Fu2, Xiaojun Wang3

  • 1School of Medicine and Health Management, Huazhong University of Science and Technology, Wuhan, Hubei, China.

BMJ Open
|July 1, 2026
PubMed
Summary

This study uses discrete choice experiments to understand what tuberculosis physicians value in artificial intelligence (AI) explanations. Findings will guide the development of trustworthy AI tools for TB diagnosis.

Keywords:
Artificial IntelligenceHealth informaticsTuberculosis

Related Experiment Videos

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Health Services Research

Background:

  • Artificial intelligence (AI) shows promise in tuberculosis (TB) diagnosis, but clinical uptake hinges on physician trust and understanding.
  • Explainable AI (XAI) aims to enhance trust, yet specific physician preferences for XAI features in TB diagnosis remain underexplored.
  • Understanding these preferences is crucial for developing user-centered AI diagnostic tools.

Purpose of the Study:

  • To investigate tuberculosis physicians' preferences for explainable AI (XAI) attributes in diagnostic settings.
  • To quantify the trade-offs physicians are willing to make among different AI explainability features.
  • To inform the development and implementation of effective XAI systems for TB diagnosis.

Main Methods:

  • A discrete choice experiment (DCE) was designed using a D-efficient approach.
  • Six key attributes of AI explainability were identified through literature review, interviews, and expert consultation.
  • Preference data from TB physicians in Hubei Province, China, will be analyzed using multinomial and mixed logit models.

Main Results:

  • The study protocol outlines the methodology for analyzing physician preferences for AI explainability attributes.
  • The analysis will estimate the relative importance of different explainability features.
  • Heterogeneity in preferences across physician subgroups will be explored.

Conclusions:

  • This research protocol details a DCE to elicit TB physicians' preferences for AI explainability.
  • The findings will guide the design of user-centered XAI systems, fostering greater adoption of AI in TB diagnostics.
  • Dissemination aims to improve AI implementation in clinical practice.