Artificial Intelligence in Radiology: Opportunities and Challenges

Marta N Flory; Sandy Napel; Emily B Tsai

doi:10.1053/j.sult.2024.02.004

Artificial Intelligence in Radiology: Opportunities and Challenges

Semin Ultrasound CT MR. 2024 Feb 23:S0887-2171(24)00005-2. doi: 10.1053/j.sult.2024.02.004. Online ahead of print.

Authors

Marta N Flory¹, Sandy Napel¹, Emily B Tsai²

Affiliations

¹ Department of Radiology, Stanford University School of Medicine, Center for Academic Medicine, Palo Alto, CA.
² Department of Radiology, Stanford University School of Medicine, Center for Academic Medicine, Palo Alto, CA. Electronic address: ebtsai@stanford.edu.

PMID: 38403128
DOI: 10.1053/j.sult.2024.02.004

Abstract

Artificial intelligence's (AI) emergence in radiology elicits both excitement and uncertainty. AI holds promise for improving radiology with regards to clinical practice, education, and research opportunities. Yet, AI systems are trained on select datasets that can contain bias and inaccuracies. Radiologists must understand these limitations and engage with AI developers at every step of the process - from algorithm initiation and design to development and implementation - to maximize benefit and minimize harm that can be enabled by this technology.

Publication types

Review