腾讯与伦敦医疗公司合作 用AI诊断帕金森症

腾讯将人工智能触角延伸至伦敦的医疗产业,引起了外媒BBC的关注报道。BBC指出,伦敦医疗公司Medopad与中国科技巨头腾讯联手合作,运用人工智能来诊断帕金森症。

在美国和中国正在争夺人工智能竞赛的领导地位的时候,中国和英国联手合作,确实引起了业界的注目。

腾讯医学人工智能实验室主任Wei Fan博士表示:“我们使用人工智能来测量帕金森病患者的恶化,患者不需要佩戴任何传感器或设备。”

该项目的目标是加快运动功能的评估过程,以往这个部分通常需要半个多小时。而使用Medopad开发的智能手机技术,希望患者可以在三分钟内接受评估——甚至可能不必去医院就诊。

全世界有多达1000万人被诊断为帕金森氏症,这个数字还可能会继续增长,因为这是老年人中常见的一种疾病。人工智能技术的进步,将有望使医生更容易开出治疗来缓解症状。

报道称,该系统通过人工智能技术,摄像头可以捕捉患者移动手的方式,以确定症状的严重程度。目前,研究小组已经对该系统进行了培训。

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" 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Medopad是一家位于伦敦的公司,致力于开发应用程序和可穿戴设备,以监控各种疾病患者。BBC指出,尽管该公司不断快速增长,与腾讯这样的巨型企业相比,Medopad的体量仍相当 “迷你”。

Medopad的首席执行官Dan Vahdat表示,没有一家英国公司可以与腾讯作为合作伙伴提供的服务相匹敌。“我们的目标是影响全球十亿患者。为了能够达到这种规模,我们需要与具有国际影响力的合作伙伴合作”。

当然,Medopad并非唯一使用新技术治疗帕金森病患者的企业。BBC指出,瑞士机器人研究所的两名研究人员,正在开发一款智能手机应用程序,该应用程序将要求患者进行各种锻炼,然后使用机器学习分析结果。