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Digital Biomarkers for Neurodegenerative Disease.用于神经退行性疾病的数字生物标志物
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3
Integrating large language models in care, research, and education in multiple sclerosis management.将大型语言模型整合到多发性硬化症管理的护理、研究和教育中。
Mult Scler. 2024 Oct;30(11-12):1392-1401. doi: 10.1177/13524585241277376. Epub 2024 Sep 23.
4
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5
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Front Neurol. 2024 Jun 20;15:1407257. doi: 10.3389/fneur.2024.1407257. eCollection 2024.
6
ChatGPT vs. neurologists: a cross-sectional study investigating preference, satisfaction ratings and perceived empathy in responses among people living with multiple sclerosis.ChatGPT 与神经科医生:一项横断面研究,调查多发性硬化症患者对偏好、满意度评分和感知同理心的反应。
J Neurol. 2024 Jul;271(7):4057-4066. doi: 10.1007/s00415-024-12328-x. Epub 2024 Apr 3.
7
The challenge of using patient reported outcome measures in clinical practice: how do we get there?在临床实践中使用患者报告结局测量的挑战:我们如何做到这一点?
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人工智能与患者输入科学:来自多发性硬化症患者的视角

Artificial intelligence and science of patient input: a perspective from people with multiple sclerosis.

作者信息

Helme Anne, Kalra Dipak, Brichetto Giampaolo, Peryer Guy, Vermersch Patrick, Weiland Helga, White Angela, Zaratin Paola

机构信息

Multiple Sclerosis International Federation, London, United Kingdom.

Dept. Medical Informatics & Statistics, The European Institute for Innovation through Health Data, Ghent University Hospital, Gent, Belgium.

出版信息

Front Immunol. 2025 Feb 17;16:1487709. doi: 10.3389/fimmu.2025.1487709. eCollection 2025.

DOI:10.3389/fimmu.2025.1487709
PMID:40034708
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11872699/
Abstract

Artificial intelligence (AI) can play a vital role in achieving a shift towards predictive, preventive, and personalized medicine, provided we are guided by the science with and of patient input. Patient-reported outcome measures (PROMs) represent a unique opportunity to capture experiential knowledge from people living with health conditions and make it scientifically relevant for all other stakeholders. Despite this, there is limited uptake of the use of standardized outcomes including PROMs within the research and healthcare system. This perspective article discusses the challenges of using PROMs at scale, with a focus on multiple sclerosis. AI approaches can enable learning health systems that improve the quality of care by examining the care health systems presently give, as well as accelerating research and innovation. However, we argue that it is crucial that advances in AI - whether relating to research, clinical practice or health systems policy - are not developed in isolation and implemented 'to' people, but in collaboration 'with' them. This implementation of science with patient input, which is at the heart of the Global PROs for Multiple Sclerosis (PROMS) Initiative, will ensure that we maximize the potential benefits of AI for people with MS, whilst avoiding unintended consequences.

摘要

人工智能(AI)在实现向预测性、预防性和个性化医疗的转变中可以发挥至关重要的作用,前提是我们以科学为指导,并纳入患者的意见。患者报告结局测量(PROMs)提供了一个独特的机会,可以从患有健康问题的人群中获取经验知识,并使其对所有其他利益相关者具有科学相关性。尽管如此,在研究和医疗系统中,包括PROMs在内的标准化结局的使用情况仍然有限。这篇观点文章讨论了大规模使用PROMs所面临的挑战,重点是多发性硬化症。人工智能方法可以推动学习型医疗系统的发展,通过审视当前医疗系统提供的护理来提高护理质量,同时加速研究和创新。然而,我们认为至关重要的是,人工智能的进步——无论是与研究、临床实践还是卫生系统政策相关——都不应孤立地开发并“强加于”人们,而应与他们“合作”开发。这种在患者参与下实施科学的做法是全球多发性硬化症患者报告结局(PROMS)倡议的核心,将确保我们最大限度地发挥人工智能对多发性硬化症患者的潜在益处,同时避免意外后果。