Research Letter: Application of GPT-4 to select next-step antidepressant treatment in major depression

IntroductionLarge language models perform well on a range of academic tasks including medical examinations. The performance of this class of models in psychopharmacology has not been explored. MethodChat GPT-plus, implementing the GPT-4 large language model, was presented with each of 10 previously-studied antidepressant prescribing vignettes in randomized order, with results regenerated 5 times to evaluate stability of responses. Results were compared to expert consensus. ResultsAt least one of the optimal medication choices was included among the best choices in 38/50 (76%) vignettes: 5/5 for 7 vignettes, 3/5 for 1, and 0/5 for 2. At least one of the poor choice or contraindicated medications was included among the choices considered optimal or good in 24/50 (48%) of vignettes. The model provided as rationale for treatment selection multiple heuristics including avoiding prior unsuccessful medications, avoiding adverse effects based on comorbidities, and generalizing within medication class. ConclusionThe model appeared to identify and apply a number of heuristics commonly applied in psychopharmacologic clinical practice. However, the inclusion of less optimal recommendations indicates that large language models may pose a substantial risk if routinely applied to guide psychopharmacologic treatment without further monitoring.