2022
Class syllabus 2022
Student report "Patient perspectives and robot doctors"
Student presentation "Patient perspectives and robot doctors"
2021
Class syllabus 2021
Student report "Artificial Intelligence in Hospitals"
Student presentation "Artificial Intelligence in Hospitals"
RAIDIO educational materials
Your Future Doctor may not be Human
BA honours couse at Maastricht University 2020-2022
The rapid development of artificial intelligence (AI) is considered one of the most transformative forces of our time. In this honours seminar, we will focus on one specific domain that is rapidly changing due to emerging AI applications: image-based medicine. In recent years, automated image recognition technology has become much better at analysing medical images such as x-rays, CT-scans and MRIs. These techniques are now introduced in hospitals and increasingly used by medical imaging experts, including pathologists and radiologists working in the Netherlands. In 2018, newspaper headlines expressed the excitement around this new development: “AI system beats team of 15 doctors in competition” (Walter, 2018) and “Your future doctor may not be human” (Norman, 2018). A few years later, an increasing number of scholars have begun to wonder if the hype around AI has exaggerated the benefits of what is variously called “algorithmic care,” “deep medicine” or “digital medicine.”
In this seminar, we will carefully analyse the social and ethical issues related to this ongoing debate, and we will examine the way potential threats, promises, and hopes around medical AI are framed. We would like to focus on how these developments are understood and perceived by patients. Examples of questions that could be addressed in this seminar are:
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Do patients need to know about the role of AI in their medical treatment?
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If so, what do they need to know, and who is responsible for informing them?
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Will the use of AI benefit some (groups of) patients more than others?
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How does the use of AI in diagnosis and treatment affect the patient-healthcare professional relationship?
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If AI systems are used, what happens to the notions of ‘informed consent’ and ‘second opinion’?
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What other kinds of ethical and normative questions might arise?
Students in this seminar will engage with such questions by reading and discussing relevant medical AI literature in relation to scholarship from science and technology studies, medical anthropology, digital sociology, cultural and historical studies of science, and responsible innovation studies. Students will be encouraged to become critically aware of the way issues around medical AI are framed in public and academic debates and to ask why some questions become foregrounded over others. As part of this seminar, students will be encouraged to experiment with different qualitative research methods. The final assignment of this seminar will be decided by the group, depending on the interests of the students who are participating. In 2020-21, the students produced a group project, but individual papers are also possible.
Radiotherapy technologist: Strategies for dealing with automation
BA "Think Tank Project" with society partner MAASTRO at University College Maastricht 2021
MAASTRO is an independent clinic where cancer patients are treated using radiotherapy. Radiotherapy relies on complex technology. A team of radiotherapy oncologists, clinical physicists, and radiation technologists is committed to deliver individualized radiotherapy treatment.
Radiotherapy has been a fast-developing field for many years: technological and process innovations have succeeded and sometimes amplified each other. As a result, the roles, tasks and necessary skills of the people working in radiotherapy have changed almost continuously. However, the amount of change and the strain this will put on personnel in the coming years is expected to be unprecedented. Automation, specifically driven by Artificial Intelligence (AI)-techniques, is currently entering the radiotherapy domain. New forms of automation are expected to have a significant impact on the practice of radiotherapy, particularly on the day-to-day work of radiation technologists. AI-supported applications have already started the automation of radiation treatment planning (a task that currently takes about 50% of the time of radiation technologists).
In the future, AI may also serve the (partial) automation of the actual irradiation of patients. This development raises some potentially controversial questions about patient safety, liability, responsibility, ‘trust’ in technology, etc. These examples show how AI and forms of automation are causing changes in the type of work and the variation of work for radiation technologists. This also means radiation technologists will need a different set of skills. The professional focus will probably shift from performing computational tasks to checking and controlling the equipment (while technologists still need to understand these tools). Moreover, new tools will allow for a shift to individualized decision making to optimize the individual patients' treatment. This will result in the need for less technical expertise and more expertise in medical decision making.
Currently, awareness of this oncoming changes among the group of radiation technologists is relatively low and does not seem to lead to a sense of urgency or alarm (a reaction that seems to be in line with previous research about responses to automation). However, not all employees affected by automation may feel comfortable with these changes: some might not like the way their work is changing and others might not be able (or willing) to adopt the necessary new skillset. Hence, a vital and urgent question in the MAASTRO radiotherapy department is: how should the managers at MAASTRO responsibly guide the current transformation caused by new AI and automation technologies?
Research questions:
1. Which different strategies are or have been used in health care or other industries to deal with the challenges of automation and what are the benefits and disadvantages of these strategies?
2. What model (from the literature you assembled) is most suitable for determining the impact of automation on the radiation technologists at Maastro?
3. What are your predictions on the impact and effects of the implementation of AI and automation at MAASTRO?
4. What are examples of best practices for the responsible implementation of AI at MAASTRO and what are your corresponding recommendations?
Student report "AI meets MAASTRO"
What do we think about AI in healthcare?
MSc Medical Humanities master’s thesis within the RAIDIO project
Karsten Knol is currently pursuing a MSc in Medical Humanities at Utrecht University and recently joined the RAIDIO project as a research intern to write his master’s thesis. Throughout his master, he’s explored the healthcare challenges we currently face, culminating in his thesis that studies the public perception of AI in healthcare using the film created by RAIDIO researchers, From Samples to Slides. He explains the relevance of his research:
“Artificial Intelligence (AI) has gotten a lot of attention in the media recently, mostly due to its capabilities to write texts and create images. Other uses of AI, such as in healthcare, have largely remained invisible to the public. The pathology department at the Radboud University Medical Center (Radboudumc) in Nijmegen has been making the shift to digitalization in the past years, preparing amongst others for the implementation of AI. Some aspects of the working life of a pathologist, which also happens to be largely invisible to the public, has been documented by the RAIDIO research project team in the 20-minute film From Samples to Slides. In this film, the shift from an analogue to a digital workflow is explained. A digital workflow allows for automation of several processes as well as the use of AI as a tool in diagnosing the stage of a cancer for instance. By showcasing this, the film provides a very concrete example of the use of AI in pathology. I think that such a film can prove incredibly valuable in helping people understand what role AI can play in healthcare. The film is being screened to a variety of audiences, and I will study their responses to the film, their thoughts on AI in healthcare now that they have seen an example of this, and find out how well this film works as an informative tool.”