Towards a Psychological Generalist AI: A Survey of Current Applications
  of Large Language Models and Future Prospects
Tianyu He Guanghui Fu Yijing Yu Fan Wang Jianqiang Li Qing Zhao Changwei Song Hongzhi Qi Dan Luo Huijing Zou Bing Xiang Yang
The complexity of psychological principles underscore a significant societal
challenge, given the vast social implications of psychological problems.
Bridging the gap between understanding these principles and their actual
clinical and real-world applications demands rigorous exploration and adept
implementation. In recent times, the swift advancement of highly adaptive and
reusable artificial intelligence (AI) models has emerged as a promising way to
unlock unprecedented capabilities in the realm of psychology. This paper
emphasizes the importance of performance validation for these large-scale AI
models, emphasizing the need to offer a comprehensive assessment of their
verification from diverse perspectives. Moreover, we review the cutting-edge
advancements and practical implementations of these expansive models in
psychology, highlighting pivotal work spanning areas such as social media
analytics, clinical nursing insights, vigilant community monitoring, and the
nuanced exploration of psychological theories. Based on our review, we project
an acceleration in the progress of psychological fields, driven by these
large-scale AI models. These future generalist AI models harbor the potential
to substantially curtail labor costs and alleviate social stress. However, this
forward momentum will not be without its set of challenges, especially when
considering the paradigm changes and upgrades required for medical
instrumentation and related applications.