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Ibrahim, Andrew F

0000-0003-3035-8014

Mayo Clinic

School of Medicine, Texas Tech University Health Sciences Center, Lubbock, Texas, USA.

Citations 1

Artificial Intelligence in Gastroenterology Education: DeepSeek Passes the Gastroenterology Board Examination and Outperforms Legacy ChatGPT Models
Ibrahim, Andrew , Danpanichkul, Pojsakorn , Hayek, Alberto , Paul, Elahna , Farag, Annmarie , Mansoor, Masab , Cheungpasitporn, Wisit , Othman, Mohamed O. , Ibrahim, Andrew F , Hayek, Alexander , Paul, Edwin , Cheungpasitporn, Wisit , Othman, Mohamed O , Danpanichkul, Pojsakorn , Thongprayoon, Charat

American Journal of Gastroenterology | May 20, 2025 | Volume: 121 | Issue: 4 | Pages: 1041-1043

10.14309/ajg.0000000000003552 Get It

INTRODUCTION: DeepSeek was evaluated in gastroenterology board examination performance against legacy ChatGPT offline models, which previously showed failing performance. METHODS: The performances of the DeepSeek base R1 model and search-augmented R1 model were assessed using American College of Gastroenterology self-assessments (455 questions). RESULTS: DeepSeek exceeded the passing threshold. Search-augmented DeepSeek scored 81.5% across all questions, and the R1 base model scored 77.1%. Both search-augmented and offline DeepSeek models surpassed offline ChatGPT-3 (65.1%) and ChatGPT-4 (62.4%) ( P < 0.001). DISCUSSION: DeepSeek exhibited passing performance on the gastroenterology board examination but had gaps in niche topics and image exclusion limit utility. It may supplement education if validated by specialists.