PhD-student: Jens Høj Anvik

With this multidisciplinary project we aim to build a sustainable AI platform for age-related macular degeneration, a retinal disease affecting the reading vision. Diagnosis and follow-up of this patient group are based on retinal images. This opens the possibility that AI could be helpful with image analysis. Something that could improve diagnostics of the patients and ease the workload at the Eye clinic now that we are entering an era with an aging population and, consequently, a shortage of healthcare personnel.

We have developed an AI algorithm ready for testing. The first question we need to answer is whether AI can assess disease activity as well as physicians - a prerequisite that must be met. After that, more complex issues arise, such as how AI can be integrated into one of the largest outpatient clinics, within a workflow involving elderly patients who, on average, have eight visits and treatments per year.

The PhD-project’s overall aim is to evaluate the ethical implications of the different uses of artificial intelligence in clinical healthcare, including the specific technology of the main project. The ambition is to develop knowledge needed to ensure that the clinical deployment of medical AI is in line with the ethical standards of good patient care. One of the ways in which the project approaches this goal is by critically examining the stated value of the human aspect of the clinical encounter. In cases where the physician is fully or partially replaced by a machine for a certain patient-facing task, human moral values such as empathy and compassion are at stake. This raises several moral questions regarding the value of such human moral qualities, the meaning of the ethical term “good patient care”, and the patient’s trust in the technology. In researching these moral problems, the PhD-project develops a virtue ethical theoretical perspective based on the idea of medicine as a moral activity. The PhD-project’s methods are both theoretical, through normative argumentative analysis, and empirical, using qualitative interviews.