Bibb Allen1, Sheela Agarwal2, Laura Coombs3, Christoph Wald4, Keith Dreyer5. 1. Chief Medical Officer ACR Data Science Institute, Department of Radiology, Grandview Medical Center, Birmingham, Alabama. Electronic address: bibb@mac.com. 2. Lenox Hill Radiology, New York, New York. 3. ACR Data Science Institute, Reston, Virginia. 4. Lahey Hospital and Medical Center, Boston, Massachusetts. 5. Chief Science Officer ACR Data Science Institute, Massachusetts General Hospital, Boston, Massachusetts.
Abstract
PURPOSE: The ACR Data Science Institute conducted its first annual survey of ACR members to understand how radiologists are using artificial intelligence (AI) in clinical practice and to provide a baseline for monitoring trends in AI use over time. METHODS: The ACR Data Science Institute sent a brief electronic survey to all ACR members via email. Invitees were asked for demographic information about their practice and if and how they were currently using AI as part of their clinical work. They were also asked to evaluate the performance of AI models in their practices and to assess future needs. RESULTS: Approximately 30% of radiologists are currently using AI as part of their practice. Large practices were more likely to use AI than smaller ones, and of those using AI in clinical practice, most were using AI to enhance interpretation, most commonly detection of intracranial hemorrhage, pulmonary emboli, and mammographic abnormalities. Of practices not currently using AI, 20% plan to purchase AI tools in the next 1 to 5 years. CONCLUSION: The survey results indicate a modest penetrance of AI in clinical practice. Information from the survey will help researchers and industry develop AI tools that will enhance radiological practice and improve quality and efficiency in patient care.
PURPOSE: The ACR Data Science Institute conducted its first annual survey of ACR members to understand how radiologists are using artificial intelligence (AI) in clinical practice and to provide a baseline for monitoring trends in AI use over time. METHODS: The ACR Data Science Institute sent a brief electronic survey to all ACR members via email. Invitees were asked for demographic information about their practice and if and how they were currently using AI as part of their clinical work. They were also asked to evaluate the performance of AI models in their practices and to assess future needs. RESULTS: Approximately 30% of radiologists are currently using AI as part of their practice. Large practices were more likely to use AI than smaller ones, and of those using AI in clinical practice, most were using AI to enhance interpretation, most commonly detection of intracranial hemorrhage, pulmonary emboli, and mammographic abnormalities. Of practices not currently using AI, 20% plan to purchase AI tools in the next 1 to 5 years. CONCLUSION: The survey results indicate a modest penetrance of AI in clinical practice. Information from the survey will help researchers and industry develop AI tools that will enhance radiological practice and improve quality and efficiency in patient care.
Keywords:
Artificial intelligence in clinical practice; artificial intelligence survey; barriers to implementation of artificial intelligence; market penetrance of artificial intelligence
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