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

You might also read

Related Articles

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

Sort by
Same author

Democratizing computational skills: evaluating an asynchronous microlearning framework for cloud-based data analytics in health services research.

Frontiers in public health·2026
Same author

Deep learning‑based cell type prediction in lung tissue from brightfield histology using CODEX-derived labels.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

Trends and Disparities in Mortality due to Pulmonary Embolism Among Adults With Atrial Fibrillation From 1999 to 2020: Insights From the CDCWONDER Database.

Journal of arrhythmia·2026
Same author

DigitAb: Domain-Adaptive Cell Type Prediction Method from Light Microscopy Images.

bioRxiv : the preprint server for biology·2026
Same author

Building Capacity for Rigorous Health Research Through Grant Writing Coaching.

International journal of environmental research and public health·2026
Same author

Spatially Resolved Banff Tubulitis and Glomerulitis Scoring in Kidney Allograft Biopsies via Artificial Intelligent -Based Structure Segmentation and Spatial Transcriptomics.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: Jan 15, 2026

Artificial Intelligence Approaches to Assessing Primary Cilia
08:58

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

4.1K

Shaping learning objectives for biomedical artificial intelligence: Student-centered insights into novel cell

Rachel Emily Liu-Galvin1, Nicholas Sherwin1, Selin Kavak1

  • 1Department of Health Services Research, Management, and Policy, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.

Journal of Clinical and Translational Science
|October 10, 2025
PubMed
Summary
This summary is machine-generated.

Biomedical trainees identified diverse educational uses for AI-powered cell visualization technology, FUSION. Engaging students in co-design can guide the translation of AI tools for improved learning outcomes in science education.

Keywords:
Artificial intelligenceeducational technologylearningmedicalundergraduate

More Related Videos

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

800
Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging
09:56

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

7.0K

Related Experiment Videos

Last Updated: Jan 15, 2026

Artificial Intelligence Approaches to Assessing Primary Cilia
08:58

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

4.1K
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

800
Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging
09:56

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

Published on: April 30, 2019

7.0K

Area of Science:

  • Biomedical education
  • Artificial intelligence in science
  • Educational technology

Background:

  • Artificial intelligence (AI) is increasingly used in biomedical research.
  • Curricula need to adapt to trainees' interests and AI advancements.
  • Student engagement in curriculum development enhances motivation and learning.

Purpose of the Study:

  • Explore educational applications of a novel AI-powered cell-visualization technology.
  • Investigate trainee perceptions of AI technology in undergraduate education.
  • Identify learner-centered curriculum development opportunities using AI.

Main Methods:

  • Mixed-methods approach combining elicitation interviews and cultural domain analysis.
  • Identified salient ideas regarding educational uses of Functional Unit State Identification & Navigation with Whole Slide Images (FUSION).
  • Analyzed 21 student interviews, reducing them to consensus-based statements on learning applications.

Main Results:

  • Eight clusters of 25 unique consensus-based statements were identified.
  • Students perceived FUSION as a tool for cell analysis, measurement, and diverse educational applications (medical, K-12, public engagement).
  • Demonstrated the potential of cultural consensus methods for learner-centered curriculum development.

Conclusions:

  • Trainees see numerous educational uses for FUSION beyond traditional biomedical research.
  • Findings support the integration of AI tools into biomedical curricula.
  • Engaging trainees in co-design is crucial for effective technology translation in education.