What we do
Since 2017, we study AI technologies in the medical domain, in particular medical imaging. We study how AI technologies are perceived, developed and implemented in clinical practice. As organizational scholars, we further investigate how professionals reconfigure their work and organizing practices to create social and economic value with AI.
Who we are
We are a multidisciplinary team of academics and practitioners with expertise in organization studies, management, and medicine. We strive to be critical of the hype around AI by digging into actual cases and deeply understanding the complexities around working and organizing with AI.
We are keen on sharing our findings with practitioners as well as academics.
Work together
Are you working in the field of medical imaging? Do you want to learn more about our research or do you want to get involved? Please, do not hesitate to contact Mohammad or Bomi.
Dr. Mohammad H. Rezazade Mehrizi Prof. dr. Marleen Huysman
Prof. dr. Frans Feldberg Dr. Paul R. Algra
Ms. Bomi Kim
Experiences, perceptions, and intentions of radiologists regarding AI
In this project we want to understand radiologists’ and radiographers’ experiences with AI over time. We try to grasp how professionals perceive AI’s potential and actual impact on their work and profession, and to understand their strategies for coping with rapid developments of AI applications. So far, we have conducted interviews with more than 100 professionals from the field of medical imaging (primarily radiologists) from 28 countries with diverse backgrounds, work positions, and experiences on AI.
This research has been supported by Prof. Frans Feldberg and the European Society of Medical Imaging Informatics (EuSoMII)’s scientific and management boards, particularly Dr. Paul Algra, Dr. Erik Ranschaert, and Dr. Peter van Ooijen.
Development of AI applications in the domain of medical imaging
With this project we aim to understand how startups and established firms develop and position their AI applications. We have studied all AI applications that are offered in the market in the medical imaging field. We analyzed all applications based on their features, functionalities, target body part, and modality. In addition, we examined the regulatory status of these applications (e.g., having FDA or CE approval). We also looked into how these applications seek to impact the work of radiologists (e.g., automate, augment, or extend), in which form these applications are offered to work (e.g., on-premise, cloud-based), as well as integration issues with the workflow (e.g., integrated with PACS/RIS).
Our latest update as of December 2019 includes more than 300 AI applications.
Clinical implementation of AI applications
In this project we aim to learn more about the actual clinical implementations of AI applications. For this purpose, we collaborate with two radiology departments that are considered to be leading examples of AI implementation in the Netherlands: North-West Ziekenhuis (NWZ) and Leiden University Medical Center (LUMC). To understand how radiologists work with AI technologies, we conduct at-work observations complemented by interviews. We examine how various practices, processes, and organizational structures are reorganized in the department to support effective and fundamental implementation of AI solutions. Dr. Paul Algra is the key initiator and supporting figure of the research in NWZ. Professor Mark van Buchem is the head of the radiology department and key supporting figure of the study in LUMC.
Experiences, perceptions, and intentions of radiologists regarding AI
- Kim, Bomi & Rezazade Mehrizi, Mohammad H. (2020), “Maneuvering uncertainty and urgency in working with artificial intelligence”, AI@Work conference, Amsterdam, March 2020
- Rezazade Mehrizi, Mohammad H. (2020), “Narrow Intelligence: a socio-technical perspective of the narrowness of artificial intelligence at work”, AI@Work conference, Amstedam, March 2020
- Merel Bulder (2018), “The transformation of radiology from a knowledge and learning perspective”, Master thesis, Vrije Universiteit Amsterdam, School of Business and Economics
- Merel Bulder, (2018), “De houding van de radioloog ten opzichte van artificiële intelligentie”, Memorad, vol. 3. 29-31.
- Felix Worringen (2019), “The rise of artificial intelligence in radiology: How do the perceived (in-)capabilities of artificial intelligence shape a perceived change in the set of skills performed by a radiologist?”, Master thesis, Vrije Universiteit Amsterdam, School of Business and Economics.
- Lorena Tol (2019), “Framing disruptive technology: How radiologists frame Artificial Intelligence technology and reframe their current role and work in the organization”, Master thesis, Vrije Universiteit Amsterdam, School of Business and Economics.
- Ann Nguyen (2019), “The perceived influence of technological capabilities and characteristics of skills on the radiologists’ perception about the impact of AI on their professional skills”, Master thesis, Vrije Universiteit Amsterdam, School of Business and Economics.
- Diana Diaz Curiel (2019), “Artificial Intelligence in Radiology: The impact of Trust”, Master thesis, Vrije Universiteit Amsterdam, School of Business and Economics.
Development of AI applications in the domain of medical imaging
- Milou Homan (2019), “AI in radiology: An assessment of intelligent solutions and their potential impact on errors within the diagnostic workflow”, Master thesis, Vrije Universiteit Amsterdam, School of Business and Economics.
- Ayumindya Paramita Dewi, (2019), “The roles of artificial intelligence in diagnosis workflow in neuroradiology”, Master thesis, Vrije Universiteit Amsterdam, School of Business and Economics.
