Extracting common processes across psychotherapies through AI.

INTRODUCTION: Psychological treatments are effective in addressing mental disorders. However, they often remain confined within two primary limitations: diagnostic categories and theoretical frameworks. This study aimed to investigate how artificial intelligence could help transcend these boundaries. METHOD: We recruited 125 psychotherapists. For each psychotherapeutic approach, we asked them to describe: (1) the proposed processes underlying psychopathology; (2) the techniques employed in treatment; and (3) potential factors contributing to resistance. RESULTS: By applying topic modeling techniques on the psychotherapists' discourse, we identified 30 topics related to psychopathological or psychotherapeutic processes (e.g., emotional regulation). Notably, none of these topics were exclusively associated with a single psychotherapeutic approach, suggesting that psychotherapies share common underlying processes. CONCLUSION: Artificial Intelligence techniques can help to identify shared processes across various psychotherapeutic approaches. This proof of concept suggests that further research utilizing these methods is warranted.