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.

翻译：本文献综述探讨了利用AI沟通辅助工具检测情绪障碍（如双相情感障碍）——一种患者经历剧烈情绪波动的精神疾病。由于该疾病的定义模糊性，即使精神科医生也难以对其诊断完全确信。精神与情绪状态的变化往往高度主观，难以通过短期问卷和精神科会诊精准捕捉。对许多患者而言，基于试错法的诊断与治疗几乎不可避免。为了制定及时且全面的诊疗方案，患者与精神科医生必须在整个过程中保持同等程度的参与。这一结论基于对以下两方面当前干预措施的详细评估：（i）疾病定义的模糊性，以及（ii）药物与诊断试错需求。据此，我提出通过实施一种体现共享决策模型的AI沟通辅助工具，以辅助双相情感障碍的准确诊断与治疗。我建议将该模型分解为特定的关键决策节点，为流程中的各参与方提供考量，并与服务蓝图模型保持一致。最后，我强调AI在双相情感障碍诊断与治疗中的重要性，因其能够兼顾患者异质性，并弥合精神医疗与人机交互之间的鸿沟。