  The current work investigates the capability of Large language models (LLMs)
that are explicitly trained on large corpuses of medical knowledge (Med-PaLM 2)
to predict psychiatric functioning from patient interviews and clinical
descriptions without being trained to do so. To assess this, n = 145 depression
and n =115 PTSD assessments and n = 46 clinical case studies across high
prevalence/high comorbidity disorders (Depressive, Anxiety, Psychotic, trauma
and stress, Addictive disorders) were analyzed using prompts to extract
estimated clinical scores and diagnoses. Results demonstrate that Med-PaLM 2 is
capable of assessing psychiatric functioning across a range of psychiatric
conditions with the strongest performance being the prediction of depression
scores based on standardized assessments (Accuracy range= 0.80 - 0.84) which
were statistically indistinguishable from human clinical raters t(1,144) =
1.20; p = 0.23. Results show the potential for general clinical language models
to flexibly predict psychiatric risk based on free descriptions of functioning
from both patients and clinicians.
