Practical Guide to Machine Learning and Artificial Intelligence in Surgical Education Research

Daniel A Hashimoto^{1

2}, Julian Varas³, Todd A Schwartz^{4

5}

Affiliations

¹ Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia.
² Department of Computer and Information Science, University of Pennsylvania School of Engineering and Applied Science, Philadelphia.
³ Centro de Cirugía Experimental y Simulación, Pontifica Universidad Católica, Santiago, Chile.
⁴ Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill.
⁵ Statistical Editor, JAMA Surgery.

Comment

Daniel A Hashimoto et al. JAMA Surg. 2024.

. 2024 Apr 1;159(4):455-456.

doi: 10.1001/jamasurg.2023.6687.

Daniel A Hashimoto^{1

2}, Julian Varas³, Todd A Schwartz^{4

5}

¹ Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia.
² Department of Computer and Information Science, University of Pennsylvania School of Engineering and Applied Science, Philadelphia.
³ Centro de Cirugía Experimental y Simulación, Pontifica Universidad Católica, Santiago, Chile.
⁴ Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill.
⁵ Statistical Editor, JAMA Surgery.

No abstract available

Plain language summary

This Guide to Statistics and Methods gives an overview of artificial intelligence techniques and tools in surgical education research.

Improving the Integrity of Surgical Education Scholarship.
Cochran A, Stefanidis D, Kibbe MR. Cochran A, et al. JAMA Surg. 2024 Mar 1;159(3):237-238. doi: 10.1001/jamasurg.2023.6672. JAMA Surg. 2024. PMID: 38170480 No abstract available.