Wenk Judith, Voigt Isabel, Inojosa Hernan, Schlieter Hannes, Ziemssen Tjalf
Center of Clinical Neuroscience, Department of Neurology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany.
Research Group Digital Health, Faculty of Business and Economics, Technische Universität Dresden, Dresden, Germany.
Front Immunol. 2024 Feb 15;15:1356436. doi: 10.3389/fimmu.2024.1356436. eCollection 2024.
Recent advances in the field of artificial intelligence (AI) could yield new insights into the potential causes of multiple sclerosis (MS) and factors influencing its course as the use of AI opens new possibilities regarding the interpretation and use of big data from not only a cross-sectional, but also a longitudinal perspective. For each patient with MS, there is a vast amount of multimodal data being accumulated over time. But for the application of AI and related technologies, these data need to be available in a machine-readable format and need to be collected in a standardized and structured manner. Through the use of mobile electronic devices and the internet it has also become possible to provide healthcare services from remote and collect information on a patient's state of health outside of regular check-ups on site. Against this background, we argue that the concept of pathways in healthcare now could be applied to structure the collection of information across multiple devices and stakeholders in the virtual sphere, enabling us to exploit the full potential of AI technology by e.g., building digital twins. By going digital and using pathways, we can virtually link patients and their caregivers. Stakeholders then could rely on digital pathways for evidence-based guidance in the sequence of procedures and selection of therapy options based on advanced analytics supported by AI as well as for communication and education purposes. As far as we aware of, however, pathway modelling with respect to MS management and treatment has not been thoroughly investigated yet and still needs to be discussed. In this paper, we thus present our ideas for a modular-integrative framework for the development of digital patient pathways for MS treatment.
人工智能（AI）领域的最新进展可能会为多发性硬化症（MS）的潜在病因及影响其病程的因素带来新的见解，因为人工智能的应用不仅从横断面角度，而且从纵向角度为大数据的解读和使用开辟了新的可能性。对于每一位MS患者，随着时间的推移会积累大量的多模态数据。但要应用人工智能及相关技术，这些数据需要以机器可读的格式提供，并且需要以标准化和结构化的方式收集。通过使用移动电子设备和互联网，也能够从远程提供医疗保健服务，并在常规现场检查之外收集患者的健康状况信息。在此背景下，我们认为现在医疗保健中的路径概念可用于构建虚拟领域中跨多个设备和利益相关者的信息收集结构，使我们能够通过例如构建数字孪生体来充分发挥人工智能技术的潜力。通过数字化和使用路径，我们可以在虚拟层面将患者及其护理人员联系起来。利益相关者随后可以依靠数字路径，在基于人工智能支持的高级分析进行程序排序和治疗方案选择时获得循证指导，以及用于沟通和教育目的。然而，据我们所知，关于MS管理和治疗的路径建模尚未得到充分研究，仍需进行讨论。因此，在本文中，我们提出了关于开发MS治疗数字患者路径的模块化集成框架的想法。
