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Enabling the Development and Application of Artificial Intelligence in the NHS

推动人工智能在 NHS 中的开发和应用

基本信息

批准号：
MR/T019050/1
负责人：
Pearse Keane
金额：
$ 137.73万
依托单位：
University College London
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FT019050%2F1
关键词：
Enabling Development Application Artificial Intelligence

项目摘要

THE PROMISE OF AI IN HEALTHCAREArtificial intelligence (AI) is a field of study which tries to get computers to behave in ways we would consider intelligent if those same behaviours were exhibited by humans - for example, the replication of human cognitive skills such as problem solving. But AI has huge potential beyond the mimicking of human behaviours - it is a fundamental technology that can allow meaningful processing of data beyond the comprehension of the human brain. The promise of AI for healthcare is thus clear - it could allow every diagnosis and treatment to be personalized on the basis of all known information about a patient, incorporating lessons from collective experience. By streamlining workflows, providing automated diagnosis of routine, non-serious conditions, and by allowing liberation from keyboards, AI could ultimately provide healthcare professionals the "gift of time" - the dedicated time really required to provide the best possible care for patients.OBJECTIVEMuch of the exceptional recent progress in the application of AI to healthcare has come in the diagnosis of eye disease. This includes work that I initiated and led - the collaboration between Moorfields Eye Hospital and Google DeepMind to apply AI to retinal diseases such as age-related macular degeneration (AMD). My fundamental objective in this fellowship will be to drive the development and application of AI on the NHS using ophthalmology as a model. By emphasizing a central role for patients, and a thoughtful approach to use of their data, this fellowship will provide experience that can be shared with other medical specialties, helping the NHS become a world leader in AI. TRAINING AND DEVELOPMENTThis fellowship will allow me to become a world leader in the application of AI to healthcare, developing the leadership and technical skills to lead a diverse and multi-disciplinary research group, establish collaborations, and drive innovation.CASE FOR SUPPORTThe infrastructure component will focus on development of an ophthalmic bioresource that can be used to build AI systems and to evaluate their clinical performance. Using this, I will lead novel approaches to educating patients about how their data is used to develop AI systems. The research component will begin by investigating the feasibility in healthcare of "AI that can build AI" - recently developed platforms that can allow healthcare professionals without any computer programming experience to explore AI. It will next focus on developing novel AI systems that can provide more individualized treatment of retinal diseases like AMD, can enhance and transform ophthalmic images to allow better diagnosis, and which can generate "imitation" ophthalmic images indistinguishable from real versions. Finally, the research component will focus on links between the eye and the rest of the body, using AI in an attempt to predict the future development of diseases such as Alzheimer's, stroke, and heart attack.Finally, in years 5-7 of the fellowship, I plan to focus more on the practical implementation of AI systems in patient care pathways in the NHS and around the world. POTENTIAL APPLICATIONS AND BENEFITSThis fellowship will benefit the academic community and industry by greatly facilitating the development of AI systems for ophthalmology. Through the creation of benchmark datasets, it will assist regulators to ensure the safety and effectiveness of AI systems before they are implemented in the real world. Most importantly, these systems will ultimately provide direct benefits for patients with better diagnosis and treatment of eye disease. The NHS will also benefit by allowing hospital eye services to become both more efficient and less expensive. Finally, this fellowship will benefit the public and policy makers by developing and disseminating best practice regarding the use of patient data in AI, as well as allowing them to visualize and contextualize its use.

人工智能(AI)的前景人工智能(AI)是一个研究领域，它试图让计算机以我们认为是智能的方式行事--如果同样的行为在人类身上表现出来的话--例如，复制人类的认知技能，如解决问题。但人工智能除了模仿人类行为之外，还有巨大的潜力--它是一项基础技术，可以对人脑无法理解的数据进行有意义的处理。因此，人工智能在医疗保健方面的前景是明确的-它可以允许根据所有有关患者的已知信息，结合集体经验的教训，对每一项诊断和治疗进行个性化。通过简化工作流程，提供常规、非严重疾病的自动诊断，并允许从键盘中解放出来，人工智能最终可以为医疗专业人员提供“时间的礼物”--为患者提供尽可能好的护理所真正需要的专门时间。这包括我发起和领导的工作-Moorfield眼科医院和Google DeepMind之间的合作，将人工智能应用于视网膜疾病，如老年性黄斑变性(AMD)。我在这次奖学金中的基本目标将是以眼科为模型推动人工智能在NHS上的发展和应用。通过强调患者的核心作用，以及对他们数据的深思熟虑的使用方法，这一奖学金将提供可以与其他医学专业分享的经验，帮助NHS成为人工智能领域的世界领导者。培训和发展这一奖学金将使我成为将人工智能应用于医疗保健的世界领导者，培养领导能力和技术技能，以领导一个多样化和多学科的研究小组，建立合作，并推动创新。CASE for Support基础设施部分将专注于开发可用于构建AI系统和评估其临床表现的眼科生物资源。利用这一点，我将领导新的方法来教育患者如何使用他们的数据来开发人工智能系统。研究部分将首先调查可以构建人工智能的人工智能在医疗保健领域的可行性--这是最近开发的平台，可以让没有任何计算机编程经验的医疗保健专业人员探索人工智能。下一步，它将专注于开发新型人工智能系统，这些系统可以为AMD等视网膜疾病提供更个性化的治疗，可以增强和转换眼科图像以实现更好的诊断，并且可以生成与真实版本难以区分的“模拟”眼科图像。最后，研究部分将专注于眼睛和身体其他部分之间的联系，使用人工智能试图预测阿尔茨海默氏症、中风和心脏病发作等疾病的未来发展。最后，在奖学金的5-7年，我计划更多地关注人工智能系统在NHS和世界各地患者护理路径中的实际实施。潜在的应用和BENEFITS他的奖学金将极大地促进眼科人工智能系统的发展，从而使学术界和业界受益。通过创建基准数据集，它将帮助监管机构确保人工智能系统在现实世界中实施之前的安全性和有效性。最重要的是，这些系统最终将为眼病患者提供更好的诊断和治疗的直接好处。NHS还将通过允许医院眼科服务变得更有效率和更便宜而受益。最后，这一奖学金将通过开发和传播有关在人工智能中使用患者数据的最佳实践，以及允许他们可视化和背景化其使用，从而使公众和政策制定者受益。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Stakeholder Perspectives on Clinical Decision Support Tools to Inform Clinical Artificial Intelligence Implementation: Protocol for a Framework Synthesis for Qualitative Evidence.

DOI：
10.2196/33145
发表时间：
2022-04-01
期刊：
JMIR RESEARCH PROTOCOLS
影响因子：
1.7
作者：
Al-Zubaidy, Mohaimen;Hogg, H. D. Jeffry;Maniatopoulos, Gregory;Talks, James;Teare, Marion Dawn;Keane, Pearse A.;Beyer, Fiona R.
通讯作者：
Beyer, Fiona R.

Stakeholder Perspectives on Clinical Decision Support Tools to Inform Clinical Artificial Intelligence Implementation: Protocol for a Framework Synthesis for Qualitative Evidence (Preprint)

利益相关者对临床决策支持工具的看法，以指导临床人工智能的实施：定性证据框架综合协议（预印本）