  Current AI approaches have frequently been used to help personalize many
aspects of medical experiences and tailor them to a specific individuals'
needs. However, while such systems consider medically-relevant information,
they ignore socially-relevant information about how this diagnosis should be
communicated and discussed with the patient. The lack of this capability may
lead to mis-communication, resulting in serious implications, such as patients
opting out of the best treatment. Consider a case in which the same treatment
is proposed to two different individuals. The manner in which this treatment is
mediated to each should be different, depending on the individual patient's
history, knowledge, and mental state. While it is clear that this communication
should be conveyed via a human medical expert and not a software-based system,
humans are not always capable of considering all of the relevant aspects and
traversing all available information. We pose the challenge of creating
Intelligent Agents (IAs) to assist medical service providers (MSPs) and
consumers in establishing a more personalized human-to-human dialogue.
Personalizing conversations will enable patients and MSPs to reach a solution
that is best for their particular situation, such that a relation of trust can
be built and commitment to the outcome of the interaction is assured. We
propose a four-part conceptual framework for personalized social interactions,
expand on which techniques are available within current AI research and discuss
what has yet to be achieved.
