Differentiating between GPT-generated and human-written feedback for radiology residents
Bibliographic record
Abstract
PURPOSE: Recent competency-based medical education (CBME) implementation within Canadian radiology programs has required faculty to conduct more assessments. The rise of narrative feedback in CBME, coinciding with the rise of large language models (LLMs), raises questions about the potential of these models to generate informative comments matching human experts and associated challenges. This study compares human-written feedback to GPT-3.5-generated feedback for radiology residents, and how well raters can differentiate between these sources. METHODS: Assessments were completed by 28 faculty members for 10 residents within a Canadian Diagnostic Radiology program (2019-2023). Comments were extracted from Elentra, de-identified, and parsed into sentences, of which 110 were randomly selected for analysis. 11 of these comments were entered into GPT-3.5, generating 110 synthetic comments that were mixed with actual comments. Two faculty raters and GPT-3.5 read each comment to predict whether it was human-written or GPT-generated. RESULTS: Actual comments from humans were often longer and more specific than synthetic comments, especially when describing clinical procedures and patient interactions. Source differentiation was more difficult when both feedback types were similarly vague. Low agreement (k=-0.237) between responses provided by GPT-3.5 and humans was observed. Human raters were also more accurate (80.5 %) at identifying actual and synthetic comments than GPT-3.5 (50 %). CONCLUSION: Currently, GPT-3.5 cannot match human experts in delivering specific, nuanced feedback for radiology residents. Compared to humans, GPT-3.5 also performs worse in distinguishing between actual and synthetic comments. These insights could guide the development of more sophisticated algorithms to produce higher-quality feedback, supporting faculty development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.052 | 0.272 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".