What is an Optimal Diagnosis?
This work addresses a foundational gap in diagnostic reasoning for AI and medical systems, but it is incremental as it critiques and refines existing approaches rather than introducing a new method.
The paper argues that existing definitions of diagnosis in diagnostic reasoning are incomplete because they ignore the utility of outcomes and the purpose of the diagnosis, showing that different definitions yield different qualitative meanings even with the same data.
Within diagnostic reasoning there have been a number of proposed definitions of a diagnosis, and thus of the most likely diagnosis, including most probable posterior hypothesis, most probable interpretation, most probable covering hypothesis, etc. Most of these approaches assume that the most likely diagnosis must be computed, and that a definition of what should be computed can be made a priori, independent of what the diagnosis is used for. We argue that the diagnostic problem, as currently posed, is incomplete: it does not consider how the diagnosis is to be used, or the utility associated with the treatment of the abnormalities. In this paper we analyze several well-known definitions of diagnosis, showing that the different definitions of the most likely diagnosis have different qualitative meanings, even given the same input data. We argue that the most appropriate definition of (optimal) diagnosis needs to take into account the utility of outcomes and what the diagnosis is used for.