When algorithms replace biologists: A Discrete Choice Experiment for the valuation of risk-prediction tools in Neurodegenerative Diseases

Background. Earlier detection of neurodegenerative diseases may help patients plan for their future, achieve a better quality of life, access clinical trials and possible future disease modifying treatments. Due to recent advances in artificial intelligence (AI), a significant help can come from the computational approaches targeting diagnosis and monitoring. Yet, detection tools are still underused. We aim to investigate the factors influencing individual valuation of AI-based prediction tools. Methods. We study individual valuation for early diagnosis tests for neurodegenerative diseases when Artificial Intelligence Diagnosis is an option. We conducted a Discrete Choice Experiment on a representative sample of the French adult public (N=1017), where we presented participants with a hypothetical risk of developing in the future a neurodegenerative disease. We ask them to repeatedly choose between two possible early diagnosis tests that differ in terms of (1) type of test (biological tests vs AI tests analyzing electronic health records); (2) identity of whom communicates tests’ results; (3) sensitivity; (4) specificity; and (5) price. We study the weight in the decision for each attribute and how socio-demographic characteristics influence them. Results. Our results are twofold: respondents indeed reveal a reduced utility value when AI testing is at stake (that is evaluated to 36.08 euros in average, IC = [22.13; 50.89]) and when results are communicated by a private company (95.15 €, IC = [82.01; 109.82]). Conclusion. We interpret these figures as the shadow price that the public attaches to medical data privacy. The general public is still reluctant to adopt AI screening on their health data, particularly when these screening tests are carried out on large sets of personal data.