Utility of Generative Artificial Intelligence for Japanese Medical Interview Training: Randomized Crossover Pilot Study
Notice bibliographique
Résumé
Background: The medical interview remains a cornerstone of clinical training. There is growing interest in applying generative artificial intelligence (AI) in medical education, including medical interview training. However, its utility in culturally and linguistically specific contexts, including Japanese, remains underexplored. This study investigated the utility of generative AI for Japanese medical interview training. Objective: This pilot study aimed to evaluate the utility of generative AI as a tool for medical interview training by comparing its performance with that of traditional face-to-face training methods using a simulated patient. Methods: We conducted a randomized crossover pilot study involving 20 postgraduate year 1-2 physicians from a university hospital. Participants were randomly allocated into 2 groups. Group A began with an AI-based station on a case involving abdominal pain, followed by a traditional station with a standardized patient presenting chest pain. Group B followed the reverse order, starting with the traditional station for abdominal pain and subsequently within the AI-based station for the chest pain scenario. In the AI-based stations, participants interacted with a GPT-configured platform that simulated patient behaviors. GPTs are customizable versions of ChatGPT adapted for specific purposes. The traditional stations involved face-to-face interviews with a simulated patient. Both groups used identical, standardized case scenarios to ensure uniformity. Two independent evaluators, blinded to the study conditions, assessed participants' performances using 6 defined metrics: patient care and communication, history taking, physical examination, accuracy and clarity of transcription, clinical reasoning, and patient management. A 6-point Likert scale was used for scoring. The discrepancy between the evaluators was resolved through discussion. To ensure cultural and linguistic authenticity, all interviews and evaluations were conducted in Japanese. Results: AI-based stations scored lower across most categories, particularly in patient care and communication, than traditional stations (4.48 vs 4.95; P=.009). However, AI-based stations demonstrated comparable performance in clinical reasoning, with a nonsignificant difference (4.43 vs 4.85; P=.10). Conclusions: The comparable performance of generative AI in clinical reasoning highlights its potential as a complementary tool in medical interview training. One of its main advantages lies in enabling self-learning, allowing trainees to independently practice interviews without the need for simulated patients. Nonetheless, the lower scores in patient care and communication underline the importance of maintaining traditional methods that capture the nuances of human interaction. These findings support the adoption of hybrid training models that combine generative AI with conventional approaches to enhance the overall effectiveness of medical interview training in Japan.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,004 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».