  This literature review involves the use of AI communication facilitators to
detect mood disorders such as bipolar disorder, a psychiatric condition in
which patients experience drastic mood shifts. Due to the ill-defined nature of
the disorder, it is difficult for even a psychiatrist alone to be confident
with their diagnosis. Changes in mental and mood state are often highly
subjective and difficult to pinpoint through short-term surveys and psychiatric
consultations. For many patients, diagnosis and treatment based on
trial-and-error is unavoidable. A timely and thorough diagnosis and treatment
plan is associated with the need for an equal involvement of both the patient
and the psychiatrist throughout the process. This conclusion is reached through
a detailed assessment of current interventions for (i) the ill-defined nature
of the disorder, and (ii) the trial-and-error requirement for medication and
diagnosis. As a result, I propose the implementation of an AI communication
facilitator that can aid in the appropriate diagnosis and treatment of bipolar
disorder by embodying the shared decision-making model. I propose that the
model can be broken down into specific critical decision points with
considerations made for each party involved in the process, aligning with the
service blueprint model. I conclude by emphasizing the importance of AI in
bipolar disorder diagnosis and treatment due to its ability to embrace patient
heterogeneity, and bridge the gap between mental healthcare and human-AI
interaction.
