Trust by Design: Practical Implementation Guide

This is partly because many AI systems in healthcare make their assessments based on historical data patterns. So the recommendations are not arbitrary, but based on real information. This can create trust and reduce uncertainty. At the same time, however, it remains clear that these AI diagnoses are based on probabilities, do not represent absolute certainties and should therefore always be in the context of a medical assessment.

The acceptance – and thus the success – of these systems depends on the trust of the users. If it is missing, an application is not even critically examined, but is simply no longer used. The area of telemedicine represents the absolute acid test: Because contact is only established digitally, trust must be created through the platform’s processes. The focus is not on the doctor, but on the clear process. Users are guided, know what is happening at all times and receive a result. You follow a comprehensible process and it is precisely this reduction in uncertainty that reduces queries and cancellations.

In practice, “Trust by Design” means building systems in such a way that less has to be explained and there are no grounds for mistrust. This reduces the decision-making effort for users: they do not have to check whether a result is plausible or question whether a function is working correctly. This also has advantages for the solution providers: fewer queries result in less support effort.