AI- vs Human-Based Assessment of Medical Interview Transcripts in a Generative AI–Simulated Patient System: Cross-Sectional Validation Study
Bibliographic record
Abstract
Background: Generative artificial intelligence (AI) is increasingly used in medical education, including AI-based virtual patients to improve interview skills. However, how much AI-based assessment (ABA) differs from human-based assessment (HBA) remains unclear. Objective: This study aimed to compare the quality of clinical interview assessments generated via an ABA (GPT-o1 Pro [ABA-o1] and GPT-5 Pro [ABA-5]) with those generated via an HBA conducted by clinical instructors in an AI-based virtual patient setting. We also examined whether AI reduced evaluation time and assessed agreement across participants with different levels of clinical experience. Methods: A standardized case of leg weakness was implemented in an AI-based virtual patient. Seven participants (2 medical students, 3 residents, and 2 attending physicians) each conducted an interview with the AI patient, and transcripts were scored using the 25-item Master Interview Rating Scale (0-125). Three evaluation strategies were compared. First, GPT-o1 Pro and GPT-5 Pro scored each transcript 5 times with different random seeds to test case specificity. Processing time was logged automatically. Second, 5 blinded clinical instructors independently rated each transcript once using the same rubric. Third, reliability metrics were applied. For AI, intraclass correlation coefficients (ICCs) quantified repeatability. For humans, the ICC(2,1) was calculated. Agreement was quantified using the Pearson r, Lin concordance correlation coefficient, Bland-Altman limits of agreement, Cronbach α, and ICC. Time efficiency was expressed as mean minutes per transcript and relative percentage reduction. Results: Mean interview scores were similar across methods (ABA-o1: mean 52.1, SD 6.9; ABA-5: mean 53.2, SD 6.8; HBA: mean 53.7, SD 6.8). Agreement between ABA and HBA was strong (r=0.90; concordance correlation coefficient=0.88) with minimal bias (ABA-o1: mean 0.4, SD 2.7; ABA-5: mean 1.5, SD 5.2; limits of agreement: -4.9 to 5.7 for ABA-o1 and -8.6 to 11.7 for ABA-5). The Cronbach α was 0.81 (ABA-o1), 0.86 (ABA-5), and 0.80 (HBA); the ICC(3,1) was 0.77 (ABA-o1) and 0.82 (ABA-5); and the ICC(2,1) was 0.38 (HBA). The coefficient of variation for ABA was approximately half that of HBA (6.6% vs 13.9%). Processing time for 5 runs was 4 minutes, 19 seconds for ABA-o1 and 3 minutes, 20 seconds for ABA-5 vs 10 minutes, 16 seconds for physicians, corresponding to 58% and 67.6% reductions, respectively. Conclusions: ABA-o1 and ABA-5 produced scores closely matching HBA while demonstrating superior consistency and reliability. In the setting of virtual interview transcripts, these findings suggest that ABA may serve as a valid, rapid, and scalable alternative to HBA, reducing per-assessment time by over half. Applied strategically, AI-based scoring could enable timely feedback, improve efficiency, and reduce faculty workload. Further research is needed to confirm generalizability across broader settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".