Artificial Intelligence in Radiology: Opportunities and Challenges.

Journal: Seminars in ultrasound, CT, and MR
Published Date:

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.

Authors

  • Marta N Flory
    Department of Radiology, Stanford University School of Medicine, Center for Academic Medicine, Palo Alto, CA.
  • Sandy Napel
  • Emily B Tsai
    Department of Radiology, Stanford University School of Medicine, 453 Quarry Rd, MC 5659, Palo Alto, CA 94304.