AI- vs Human-Based Assessment of Medical Interview Transcripts in a Generative AI–Simulated Patient System: Cross-Sectional Validation Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».