Center of Clinical Neuroscience, Department of Neurology, University Hospital Carl Gustav Carus Dresden, Technical University Dresden, Dresden, Germany.
Else Kröner Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany.
Mult Scler. 2024 Oct;30(11-12):1392-1401. doi: 10.1177/13524585241277376. Epub 2024 Sep 23.
Use of techniques derived from generative artificial intelligence (AI), specifically large language models (LLMs), offer a transformative potential on the management of multiple sclerosis (MS). Recent LLMs have exhibited remarkable skills in producing and understanding human-like texts. The integration of AI in imaging applications and the deployment of foundation models for the classification and prognosis of disease course, including disability progression and even therapy response, have received considerable attention. However, the use of LLMs within the context of MS remains relatively underexplored. LLMs have the potential to support several activities related to MS management. Clinical decision support systems could help selecting proper disease-modifying therapies; AI-based tools could leverage unstructured real-world data for research or virtual tutors may provide adaptive education materials for neurologists and people with MS in the foreseeable future. In this focused review, we explore practical applications of LLMs across the continuum of MS management as an initial scope for future analyses, reflecting on regulatory hurdles and the indispensable role of human supervision.
利用生成式人工智能（AI）技术，特别是大型语言模型（LLM），在多发性硬化症（MS）的管理方面具有变革性的潜力。最近的 LLM 在生成和理解类人文本方面表现出了非凡的能力。人工智能在成像应用中的整合，以及用于疾病进程分类和预后的基础模型的部署，包括残疾进展甚至治疗反应，已经引起了相当大的关注。然而，在 MS 背景下使用 LLM 仍然相对较少。LLM 有潜力支持与 MS 管理相关的多项活动。临床决策支持系统可以帮助选择合适的疾病修正疗法；基于 AI 的工具可以利用非结构化的真实世界数据进行研究，或者虚拟导师可能会在可预见的未来为神经科医生和 MS 患者提供适应性教育材料。在本次重点综述中，我们探讨了 LLM 在 MS 管理整个连续体中的实际应用，作为未来分析的初步范围，同时考虑到监管障碍和人类监督的不可或缺作用。
