Evidence map›Paper›PMID 42326897›Full record

ArticleMedEdPORTAL : the journal of teaching and learning resources2026

Implementation of a Novel Case-Based Session for Medical Students Focused on Artificial Intelligence Ethics.

Danielle M Fernandes, Talya Lisker, Shitij Arora, Aaron Hui, Adira Hulkower, Sunit Jariwala, Janice Thomas John

Abstract read
In one paragraph

Article in MedEdPORTAL : the journal of teaching and learning resources, 2026. 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

7 authors.

Danielle M FernandesAssistant Professor, Department of Pediatrics, Albert Einstein College of Medicine.ORCID https://orcid.org/0000-0001-9196-4777
Talya LiskerMedical Student, Albert Einstein College of Medicine.
Shitij AroraProfessor, Department of Medicine, Albert Einstein College of Medicine.
Aaron HuiMedical Student, Albert Einstein College of Medicine.
Adira HulkowerAssistant Professor, Department of Epidemiology and Population Health, Albert Einstein College of Medicine.
Sunit JariwalaProfessor, Department of Medicine, Albert Einstein College of Medicine.
Janice Thomas JohnAssociate Professor, Department of Pediatrics, Albert Einstein College of Medicine.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: As artificial intelligence (AI) is integrated into health care, it is critical for physicians to understand the ethical foundations of its use in medicine so that they can provide just care to patients and use AI technology to effectively support clinical care. This novel ethics session was designed to provide students with the opportunity to discuss ethical principles related to the use of AI in medicine. Methods: We designed a case-based small-group session for preclerkship medical students as part of their required bioethics course. Interdisciplinary bioethics faculty facilitated this session. After the session, participants completed a retrospective pre-post survey with questions on a 5-point Likert scale and open-ended questions. Results: One hundred seventy students attended the session, and 94 completed the survey (response rate 55%). Students reported a stronger understanding of the ethical issues surrounding AI use in medicine following the session. Content analysis of narrative responses showed that students valued the opportunity to discuss AI ethics with peers and facilitators. Discussion: Students valued this innovative session and recommended it be repeated in future years. Data from this session demonstrate a self-reported improvement in understanding of core bioethics concepts related to AI use in medicine. This case-based small-group session offers a timely and effective approach for integrating core domains of AI ethics-bias and inequity, data privacy and patient autonomy, and potential harms of AI-into the undergraduate medical school education curriculum, providing students with a foundational understanding as they prepare to use AI throughout their careers.

Indexed as

Artificial IntelligenceBioethicsEthics, MedicalStudents, MedicalCurriculumEducation, Medical, UndergraduateHumansProblem-Based LearningRetrospective StudiesSurveys and QuestionnairesArtificial IntelligenceCase-Based LearningEthics/Bioethics

Identifiers

PMID42326897
PMCPMC13279577

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.