Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Cancer Prevention02:59

Cancer Prevention

Several factors can increase the risk of cancer in an individual. About 50% of cancer cases can be prevented by adopting a healthy lifestyle, regular exercise, eating healthy, and following a modest cancer prevention diet. Epidemiological studies have consistently shown that populations with vegetable and fruit-rich diets have reduced the incidence of cancer. On the other hand, populations who have a diet rich in animal fat, red meat, junk food, or high calories are predisposed to cancer.
Some...
Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Network analysis of factors associated with lung cancer screening behavior among high-risk rural adults in Fujian, China.

Archives of public health = Archives belges de sante publique·2026
Same author

Online Intervention on Lung Cancer Screening Among High-Risk Individuals: Pilot Intervention Study.

JMIR cancer·2026
Same author

Effectiveness of Psychosocial Interventions for Demoralization in Patients with Cancer: A Systematic Review and Meta-Analysis.

Psycho-oncology·2026
Same author

Feasibility and preliminary efficacy of an art-making program to manage fear of cancer recurrence (AM-I-FCR) in lung cancer patients: a randomized controlled pilot study.

BMC medicine·2026
Same author

The chain mediating effect of family function and resilience between cognitive reactivity and depression among postpartum women in China.

BMC pregnancy and childbirth·2025
Same author

Effectiveness of nurse-led mHealth interventions on symptom outcomes in adult patients with cancer: a systematic review and meta-analysis.

BMC nursing·2025

Related Experiment Video

Updated: Jul 3, 2026

E-Patient Counseling Trial (E-PACO): Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy
06:28

E-Patient Counseling Trial (E-PACO): Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy

Published on: August 1, 2019

Using AI to Design and Develop Online Educational Modules to Enhance Lung Cancer Screening Uptake Among High-Risk

Fang Lei1, Hua Zhao2, Feifei Huang3

  • 1School of Nursing, University of Minnesota, 308 Harvard St SE, Minneapolis, MN 55455, USA.

Cancers
|February 27, 2026
PubMed
Summary

Artificial intelligence created online educational modules to boost lung cancer screening knowledge and beliefs. These modules showed high validity and usability, leading to increased screening participation among high-risk individuals.

Keywords:
Health Belief Modeleducational interventionhealth promotionlung cancer screeningonline learningsmokers

More Related Videos

Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources
05:18

Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources

Published on: October 6, 2023

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Related Experiment Videos

Last Updated: Jul 3, 2026

E-Patient Counseling Trial (E-PACO): Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy
06:28

E-Patient Counseling Trial (E-PACO): Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy

Published on: August 1, 2019

Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources
05:18

Radiation Planning Assistant - A Web-based Tool to Support High-quality Radiotherapy in Clinics with Limited Resources

Published on: October 6, 2023

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

  • Digital Health Education
  • Artificial Intelligence in Healthcare
  • Public Health Interventions

Background:

  • Low participation rates in lung cancer screening persist despite evidence for low-dose computed tomography (LDCT).
  • Educational interventions are crucial for addressing knowledge, attitude, and belief gaps influencing screening uptake.
  • High-risk individuals require targeted strategies to improve adherence to lung cancer screening guidelines.

Purpose of the Study:

  • To systematically design and develop AI-generated online educational modules for lung cancer screening.
  • To enhance knowledge, attitudes, and beliefs regarding lung cancer screening among high-risk populations.
  • To evaluate the content validity, usability, and effectiveness of AI-developed educational tools.

Main Methods:

  • Development of five interactive online modules using AI, guided by the Health Belief Model and digital health principles.
  • Content validation by an expert panel (CVI = 0.96) and usability testing with high-risk individuals (mean SUS score = 88/100).
  • Qualitative interviews and pilot testing were conducted to refine module design and content.

Main Results:

  • All modules achieved excellent content validity (I-CVI range = 0.90-1.00) and high usability ratings.
  • Participants showed significant improvements in knowledge (p < 0.001), reduced stigma (p < 0.001), and enhanced health beliefs (p < 0.001).
  • A 3-month follow-up revealed that 59.1% of participants obtained LDCT screening.

Conclusions:

  • AI-powered online modules are a valid and usable tool for improving lung cancer screening knowledge and attitudes.
  • These educational interventions show promise in increasing screening participation among high-risk individuals.
  • The developed modules offer a feasible approach for future large-scale intervention studies promoting lung cancer screening.