Mortensen Genevieve A, Zhu Rui
Indiana University, Bloomington, Indiana, USA.
Yale University, New Haven, Connecticut, USA.
AMIA Jt Summits Transl Sci Proc. 2025 Jun 10;2025:365-374. eCollection 2025.
Diagnosing Alzheimer's Disease (AD) early and cost-effectively is crucial. Recent advancements in Large Language Models (LLMs) like ChatGPT have made accurate, affordable AD detection feasible. Yet, HIPAA compliance and the challenge of integrating these models into hospital systems limit their use. Addressing these constraints, we introduce ADetectoLocum, an open-source LLM equipped model designed for AD risk detection within hospital environments. This model evaluates AD risk through spontaneous patient speech, enhancing diagnostic processes without external data exchange. Our approach secures local deployment and significantly surpasses previous models in predictive accuracy for AD detection, especially in early-stage identification. ADetectoLocum therefore offers a reliable solution for AD diagnostics in healthcare institutions.
早期且经济高效地诊断阿尔茨海默病（AD）至关重要。像ChatGPT这样的大语言模型（LLMs）的最新进展使准确且经济实惠的AD检测成为可能。然而，符合健康保险流通与责任法案（HIPAA）以及将这些模型集成到医院系统中的挑战限制了它们的使用。为了解决这些限制，我们推出了ADetectoLocum，这是一个配备大语言模型的开源模型，专为医院环境中的AD风险检测而设计。该模型通过患者的自发语音评估AD风险，在无需外部数据交换的情况下增强诊断过程。我们的方法确保了本地部署，并且在AD检测的预测准确性方面显著超越了以前的模型，尤其是在早期识别方面。因此，ADetectoLocum为医疗机构中的AD诊断提供了一个可靠的解决方案。
