Evidence map›Paper›PMID 40621296›Full record

ArticleCureus2025

Impact of a Three-Week ChatGPT 3.5-Generated Case-Based Professionalism Curriculum for Internal Medicine Residents at a United States Medical Residency: A Pilot Study.

Jared J Bies, Sukhila Reddy, Monica Botros, Andrew C Stuart, Alok K Dwivedi, Brian P Edwards, Lisa A Hechanova, Debabrata Mukherjee, Abhizith Deoker

Abstract read
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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.

Jared J BiesInternal Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, USA.
Sukhila ReddyInternal Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, USA.
Monica BotrosInternal Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, USA.
Andrew C StuartPaul L. Foster School of Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, USA.
Alok K DwivediMolecular and Translational Medicine, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, USA.
Brian P EdwardsInternal Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, USA.
Lisa A HechanovaInternal Medicine/Nephrology, Texas Tech University Health Sciences Center El Paso, El Paso, USA.
Debabrata MukherjeeInternal Medicine, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, USA.
Abhizith DeokerInternal Medicine, Texas Tech University Health Sciences Center El Paso, El Paso, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveArtificial intelligence (AI), particularly language models like ChatGPT (OpenAI, San Francisco, CA), is reshaping clinical care and medical education. This study evaluated the impact of a ChatGPT 3.5-generated case-based curriculum on internal medicine residents' understanding of professionalism in a US residency program.

methodsA single-group, pre-post intervention pilot study was conducted from August 2024 to February 2025 at a US internal medicine residency program (IRB exempt, E24149). Residents from postgraduate year (PGY)-1 to PGY-3 participated in a three-week professionalism curriculum integrated into Friday ambulatory didactics. Weekly modules featured ChatGPT 3.5-generated case scenarios aligned with the Penn State Questionnaire on Professionalism (PSQP) domains, reviewed by three faculty members for clinical and ethical relevance. Residents completed one module per week via Qualtrics (Provo, UT), receiving immediate feedback. The validated 36-item PSQP was administered anonymously before and after the intervention. Pre-post differences were analyzed using unpaired t-tests adjusted for clustering based on baseline characteristics, with sensitivity analyses using log-transformed scores. Propensity score matching and cluster-adjusted logistic regression were used for subgroup analyses. Statistical significance was set at p < 0.05.

resultsA total of 37 residents completed the pre-survey, and 33 completed the post-survey. The mean age was 28.9 years (SD: 3.4), with balanced gender distribution (18 males, 19 females) and 59% non-US citizens. Residents were evenly distributed across PGY levels. After matching by age and sex, covariate balance was achieved. While all professionalism domains improved post intervention, changes were not statistically significant overall. Female residents showed significant gains in duty (p = 0.004), accountability (p = 0.037), honor (p = 0.028), and altruism (p = 0.017), with some effects persisting after matching. No significant changes were noted in male residents. Trends toward higher "much" or "great deal" responses were observed across most PSQP items (post: 61-77% vs. pre: 35-70%). Notable gains were seen in corrective action (p = 0.006), attending seminars (p = 0.003), and upholding scientific standards.

conclusionThis pilot is among the first to evaluate a ChatGPT 3.5-generated professionalism curriculum using the validated PSQP. While overall changes were not statistically significant, meaningful gains in specific domains among female residents suggest educational benefit and support gender-responsive instructional design. The low-cost, scalable format may serve as a template for institutions seeking to implement professionalism training with limited resources. Further multi-institutional studies with paired designs and long-term follow-up are warranted.

Indexed as

ai in medical educationcase-based learningchat gptprofessionalism assessmentprofessionalism in medical education

Identifiers

PMID40621296
PMCPMC12228428

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.