OpenAlex · Aktualisierung stündlich · Letzte Aktualisierung: 19.03.2026, 11:56

Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.

Examining ChatGPT Performance on USMLE Sample Items and Implications for Assessment

2023·52 Zitationen·Academic MedicineOpen Access
Volltext beim Verlag öffnen

52

Zitationen

5

Autoren

2023

Jahr

Abstract

Achieving 60% accuracy is an approximate indicator of meeting the passing standard, requiring statistical adjustments for comparison. Hence, this assessment can only suggest consistency with the passing standards for Steps 1 and 2 Clinical Knowledge, with further limitations in extrapolating this inference to Step 3. These limitations are due to variances in item difficulty and exclusion of the simulation component of Step 3 from the evaluation-limitations that would apply to any AI system evaluated on the Step 3 sample items. It is crucial to note that responses from large language models exhibit notable variations when faced with repeated inquiries, underscoring the need for expert validation to ensure their utility as a learning tool.

Ähnliche Arbeiten

Autoren

Institutionen

Themen

Artificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical ImagingHealthcare cost, quality, practices
Volltext beim Verlag öffnen