Evidence map›Paper›PMID 41069352›Full record

ArticleJournal of clinical and translational science2025

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

Rachel Emily Liu-Galvin, Nicholas Sherwin, Selin Kavak, Victoria Liwang, Samuel Border, Mishal Khan, Sanjay Jain, Pinaki Sarder, Yulia A Levites Strekalova

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of clinical and translational science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Rachel Emily Liu-GalvinDepartment of Health Services Research, Management, and Policy, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.
Nicholas SherwinDepartment of Health Services Research, Management, and Policy, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.
Selin KavakDepartment of Health Services Research, Management, and Policy, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.
Victoria LiwangDepartment of Health Services Research, Management, and Policy, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.
Samuel BorderJ. Crayton Pruitt Family Department of Biomedical Engineering, College of Engineering, University of Florida, Gainesville, FL, USA.
Mishal KhanDepartment of Health Services Research, Management, and Policy, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.
Sanjay JainJohn T. Milliken Department of Medicine, Washington University, St Louis, MO, USA.
Pinaki SarderDepartment of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA.
Yulia A Levites StrekalovaDepartment of Health Services Research, Management, and Policy, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.ORCID https://orcid.org/0000-0002-6060-1233

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence (AI) technology is rapidly entering biomedical research, and there is a need to assess and develop curricula that address trainees' learning objectives and interests. Studies of biomedical workforce development show that the prospective engagement of students in formulating educational objectives and activities improves motivation and learning outcomes. This study aimed to explore the educational applications of a novel AI-powered technology in undergraduate education. Methods: A mixed-methods approach using elicitation interviews and cultural domain analysis was applied to identify the salience of ideas around the educational uses of Functional Unit State Identification & Navigation with Whole Slide Images (FUSION), an AI-powered cell-visualization technology. Interviews from 21 students were reduced to learning application statements and assessed for cultural salience and clustering for potential educational applications. Results: Saturation was reached after 11 interviews, and analysis resulted in eight clusters of 25 unique consensus-based statements. Students thought of the technology as a tool for cell analysis and measuring, but they also viewed applications for medical and K-12 education, public engagement, and note-keeping for technology research. Methodologically, our study demonstrates the potential of cultural consensus for learner-centered curriculum development. Conclusions: Our findings suggest that trainees perceive many educational uses of FUSION, including those that fit traditional biomedical research curricula and translational applications. Trainees should be engaged in co-design to support and guide technology translation for educational use.

Indexed as

Artificial intelligenceeducational technologylearningmedicalundergraduate

Identifiers

PMID41069352
PMCPMC12505238

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.