Applications of artificial intelligence (AI) in diagnostic radiology: a technography study
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IMAGING INFORMATICS AND ARTIFICIAL INTELLIGENCE
Applications of artificial intelligence (AI) in diagnostic radiology: a technography study Mohammad Hosein Rezazade Mehrizi 1
&
Peter van Ooijen 2 & Milou Homan 1
Received: 6 April 2020 / Revised: 16 July 2020 / Accepted: 26 August 2020 # The Author(s) 2020
Abstract Objectives Why is there a major gap between the promises of AI and its applications in the domain of diagnostic radiology? To answer this question, we systematically review and critically analyze the AI applications in the radiology domain. Methods We systematically analyzed these applications based on their focal modality and anatomic region as well as their stage of development, technical infrastructure, and approval. Results We identified 269 AI applications in the diagnostic radiology domain, offered by 99 companies. We show that AI applications are primarily narrow in terms of tasks, modality, and anatomic region. A majority of the available AI functionalities focus on supporting the “perception” and “reasoning” in the radiology workflow. Conclusions Thereby, we contribute by (1) offering a systematic framework for analyzing and mapping the technological developments in the diagnostic radiology domain, (2) providing empirical evidence regarding the landscape of AI applications, and (3) offering insights into the current state of AI applications. Accordingly, we discuss the potential impacts of AI applications on the radiology work and we highlight future possibilities for developing these applications. Key Points • Many AI applications are introduced to the radiology domain and their number and diversity grow very fast. • Most of the AI applications are narrow in terms of modality, body part, and pathology. • A lot of applications focus on supporting “perception” and “reasoning” tasks. Keywords Artificial intelligence . Radiology . Workflow . Radiologists . Forecasting
Abbreviations AI Artificial intelligence CE mark European Conformity Marking COPD Chronic obstructive pulmonary disease CT Computed tomography Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00330-020-07230-9) contains supplementary material, which is available to authorized users. * Mohammad Hosein Rezazade Mehrizi [email protected] 1
2
School of Business and Economics, KIN Center for Digital Innovation, Vrije Universiteit Amsterdam, De Boelelaan 1105, VU Main Building A-wing, 5th floor, 1081 HV Amsterdam, The Netherlands Department of Radiation Oncology, Coordinator Machine Learning Lab, Data Science Center in Health (DASH), University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
ECR EU FDA MRI NA PACS RIS RSNA SIIM US X-Ray
European Conference of Radiology European Union Food and Drug Administration Magnetic resonance imaging North America Picture Archiving and Communication System Radiological Information System Radiological Society of North America Society for Imaging Informatics in Medicine United States X-Radiatio
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