Post-traumatic stress disorder (PTSD) is highly prevalent yet chronically underreported among combat-exposed military personnel. This paper presents Molhim, a culturally adapted multimodal conversational AI platform that supports purpose-specific interactions through a configurable conversational pipeline consisting of session setup, real-time dialogue with a high-fidelity virtual avatar, and post-session analysis and feedback. In this work, we examine the PTSD screening configuration of the Molhim platform in a military healthcare context. The system employs a conversational avatar driven by a large language model, integrating real-time speech recognition, visual understanding of user input, text-to-speech synthesis, and a high-fidelity human avatar to support structured multi-turn dialogue and automated post-session analysis, including administration of the PTSD Checklist for DSM-5 (PCL-5). These findings suggest the feasibility of Molhim as a conversational platform for PTSD screening and highlight design considerations for socially cooperative human-AI systems in clinical environments.

翻译：创伤后应激障碍（PTSD）在战斗暴露的军事人员中高度流行，但长期存在漏报现象。本文提出Molhim，一个经过文化适配的多模态对话式人工智能平台，通过可配置的对话流程支持特定目的交互，包括会话设置、与高保真虚拟化身进行实时对话，以及会话后分析与反馈。本研究在军事医疗背景下检验了Molhim平台的PTSD筛查配置。该系统采用由大型语言模型驱动的对话化身，集成实时语音识别、用户输入的视觉理解、文本语音合成和高保真人类化身，以支持结构化多轮对话及自动化会话后分析，包括PTSD DSM-5检查表（PCL-5）的管理。研究结果表明Molhim作为PTSD筛查对话平台的可行性，并突显了临床环境中社会协作型人机系统的设计考量。