Clinical diagnosis can be approached as a problem of conformity assessment in which the patient takes the role of the item to be assessed and the classification of the patient as unhealthy or healthy is expressed as a requirement on the true value of a measured characteristic of the patient. The classification of patients into treatment and non-treatment groups is an important task in medicine with significant societal and personal implications. In this paper, a framework for clinical diagnosis that accounts for measurement uncertainty, based on the principles of conformity assessment, is described.

The framework is illustrated for the problem of deciding the extent of myocardial blood flow abnormalities in patients based on a predefined clinical guideline using clinical data from patients participating in a cardiac Positron Emission Tomography (PET) perfusion study at the Turku PET Centre.

By combining patient data with expert insights, the framework can help less experienced clinicians make better decisions regarding patient health, serve as a starting point for further clinical investigation, and be used as a screening to categorise patients so that the most severe cases can be prioritised on the clinical list. Furthermore, the framework could be used within a machine learning classification pipeline both to label unlabelled data and to assess the quality of labelled data.