Mohammad Hosein Rezazade Mehrizi1, Peter van Ooijen2, Milou Homan3. 1. 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. m.rezazademehrizi@vu.nl. 2. 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. 3. 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.
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.
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.
Authors: Mohammad H Rezazade Mehrizi; Simon H Gerritsen; Wouter M de Klerk; Chantal Houtschild; Silke M H Dinnessen; Luna Zhao; Rik van Sommeren; Abby Zerfu Journal: Eur Radiol Date: 2022-08-18 Impact factor: 7.034
Authors: Abdulaziz A Qurashi; Rashed K Alanazi; Yasser M Alhazmi; Ahmed S Almohammadi; Walaa M Alsharif; Khalid M Alshamrani Journal: J Multidiscip Healthc Date: 2021-11-23
Authors: Hugh G Pemberton; Lara A M Zaki; Olivia Goodkin; Ravi K Das; Rebecca M E Steketee; Frederik Barkhof; Meike W Vernooij Journal: Neuroradiology Date: 2021-09-03 Impact factor: 2.804