A Comparative Analysis of Human and AI-Augmented Feedback in Anesthesiology Education.

Daniel J Rosenkrans, Katherine B Owensby, Justin C Magin, Morgan N McCain, Emily G Teeter, Samuel N Blacker, Fei Chen

Abstract read

In one paragraph

Article in The journal of education in perioperative medicine : JEPM. 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

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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.

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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.

Daniel J RosenkransThe authors are in the Department of Anesthesiology, University of North Carolina School of Medicine in Chapel Hill, NC: is an assistant professor of anesthesiology; is a research assistant; is a medical student; is a medical student; is a professor of anesthesiology; is an associate professor of anesthesiology; is an associate professor of anesthesiology.

Katherine B OwensbyThe authors are in the Department of Anesthesiology, University of North Carolina School of Medicine in Chapel Hill, NC: is an assistant professor of anesthesiology; is a research assistant; is a medical student; is a medical student; is a professor of anesthesiology; is an associate professor of anesthesiology; is an associate professor of anesthesiology.

Justin C MaginThe authors are in the Department of Anesthesiology, University of North Carolina School of Medicine in Chapel Hill, NC: is an assistant professor of anesthesiology; is a research assistant; is a medical student; is a medical student; is a professor of anesthesiology; is an associate professor of anesthesiology; is an associate professor of anesthesiology.

Morgan N McCainThe authors are in the Department of Anesthesiology, University of North Carolina School of Medicine in Chapel Hill, NC: is an assistant professor of anesthesiology; is a research assistant; is a medical student; is a medical student; is a professor of anesthesiology; is an associate professor of anesthesiology; is an associate professor of anesthesiology.

Emily G TeeterThe authors are in the Department of Anesthesiology, University of North Carolina School of Medicine in Chapel Hill, NC: is an assistant professor of anesthesiology; is a research assistant; is a medical student; is a medical student; is a professor of anesthesiology; is an associate professor of anesthesiology; is an associate professor of anesthesiology.

Samuel N BlackerThe authors are in the Department of Anesthesiology, University of North Carolina School of Medicine in Chapel Hill, NC: is an assistant professor of anesthesiology; is a research assistant; is a medical student; is a medical student; is a professor of anesthesiology; is an associate professor of anesthesiology; is an associate professor of anesthesiology.

Fei ChenThe authors are in the Department of Anesthesiology, University of North Carolina School of Medicine in Chapel Hill, NC: is an assistant professor of anesthesiology; is a research assistant; is a medical student; is a medical student; is a professor of anesthesiology; is an associate professor of anesthesiology; is an associate professor of anesthesiology.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: High-quality feedback is essential for resident development, yet barriers to its provision persist. Artificial intelligence (AI) offers a promising tool to augment feedback, potentially addressing these challenges. This study explored whether exposing attending anesthesiologists to AI-generated feedback samples before crafting their own feedback improved its quality as rated by residents. Methods: Thirty attending anesthesiologists provided feedback on 2 vignettes addressing issues of preparedness and professionalism. Before delivering feedback on the second vignette, attendings reviewed an AI-generated feedback sample. Utilizing a validated rubric, 6 blinded residents randomly evaluated the quality of feedback across 2 conditions: attendings alone (human-only) and after attendings reviewed the AI sample (AI-augmented). Feedback ratings and vignette types were compared. Residents were assessed if they could correctly identify the origin of feedback. A qualitative analysis explored attendings' perceptions of using AI for feedback. Results: AI augmentation did not significantly improve feedback quality ratings compared with human-only feedback ( Conclusions: Although AI augmentation did not significantly improve feedback quality, it showed promise as a tool for supporting feedback provision, particularly with crafting feedback on nontechnical, more subjective issues. Additional studies are needed to better understand AI as a tool for feedback enhancement.

Indexed as

anesthesiologistsArtificial intelligenceeducationfeedbackinternship and residency

Identifiers

PMID41982264

DOI10.46374/VolXXVIII_Issue1_Rosenkrans

PMCPMC13070740

What OpenQuestion holds

Textmetadata

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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.