The shortage of clinical workforce presents significant challenges in mental
healthcare, limiting access to formal diagnostics and services. We aim to
tackle this shortage by integrating a customized large language model (LLM)
into the workflow, thus promoting equity in mental healthcare for the general
population. Although LLMs have showcased their capability in clinical
decision-making, their adaptation to severe conditions like Post-traumatic
Stress Disorder (PTSD) remains largely unexplored. Therefore, we collect 411
clinician-administered diagnostic interviews and devise a novel approach to
obtain high-quality data. Moreover, we build a comprehensive framework to
automate PTSD diagnostic assessments based on interview contents by leveraging
two state-of-the-art LLMs, GPT-4 and Llama-2, with potential for broader
clinical diagnoses. Our results illustrate strong promise for LLMs, tested on
our dataset, to aid clinicians in diagnostic validation. To the best of our
knowledge, this is the first AI system that fully automates assessments for
mental illness based on clinician-administered interviews.

通过将大规模语言模型（LLM）整合到工作流程中，我们旨在解决临床人力短缺在精神保健中所面临的挑战，从而为一般人群提供公平的精神保健。虽然 LLMs 在临床决策方面已经展示了其能力，但它们在创伤后应激障碍（PTSD）等严重疾病适应方面尚未得到充分探索。因此，我们收集了 411 个临床医师进行的诊断性访谈，并设计了一种获得高质量数据的新方法。此外，我们构建了一个综合框架，通过利用两个最先进的 LLMs（GPT-4 和 Llama-2）来自动完成基于访谈内容的 PTSD 诊断评估，这具有更广泛的临床诊断潜力。我们的结果在我们的数据集上展示了 LLMs 有望协助临床医生进行诊断验证的强大潜力。据我们所知，这是第一个完全基于临床医师访谈进行心理疾病评估自动化的人工智能系统。