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

• 批准号: MR/Y011651/1
• 负责人: Pearse Keane
• 金额: $ 75.68万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FY011651%2F1
• 关键词: Enabling; Development; Application; Artificial; Intelligence

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 also provide healthcare professionals the "gift of time" - the dedicated time really required to provide the best possible care for patients.OBJECTIVEMuch of the recent progress in the application of AI to healthcare has come in the evaluation of eye disease. My fundamental vision for this fellowship will be to drive the development and application of AI-enabled healthcare, both in the NHS and globally, using ophthalmology as an exemplar for other medical specialties.TRAINING AND DEVELOPMENTRenewal of this fellowship will allow me to greatly enhance my standing as a leader in clinical AI, consolidating the leadership and technical skills I have developed to lead a multi-disciplinary research group, establish international networks, and drive innovation. CASE FOR SUPPORTFLF renewal will support a portfolio of interlinked research projects that cover the broad spectrum of clinical AI, going "from idea to algorithm" and "from code to clinic". A central focus of my team's early stage work will be on the scaling and validation of a foundation model ("RETFound'') that we have recently developed for ophthalmology. By going from 2 million to 20 million images in training, we will create a model which can be used in less common retinal diseases and which performs well across different demographic groups. My team will also use AI to learn more about the most common sight-threatening retinal diseases, such as age-related macular degeneration (AMD) and diabetic retinopathy. We will develop systems that can predict disease progression, treatment burden, and visual outcomes, allowing better treatment and reducing sight loss. We will also continue to explore the emerging field of "oculomics" - using AI in an attempt to predict the future development of systemic diseases such as Alzheimer's, stroke, and heart attack. In parallel, my team will explore the clinical validation and translation of the most promising AI systems identified from our early stage exploratory work. This will involve evaluations of diagnostic accuracy and clinical safety, closely linked with requirements for regulatory approval and subsequent health services delivery. POTENTIAL APPLICATIONS AND BENEFITSRenewal of this fellowship will benefit the research community through the further development of AI systems in ophthalmology, making them open source or working with industry partners to explore commercialisation as appropriate. In tandem, renewal will allow development of new approaches to validation, both in-silico and in clinical studies. 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, treatment, and monitoring of eye disease, as well as potential screening for systemic disease. Finally, the NHS will benefit by reducing pressures on already over-stretched hospital eye services, reducing the risk of patients losing vision unnecessarily.

人工智能（AI）是一个研究领域，它试图让计算机以我们认为智能的方式行事，如果人类表现出同样的行为-例如，复制人类的认知技能，如解决问题。但人工智能在模仿人类行为之外还有巨大的潜力--它是一项基础技术，可以对超出人类大脑理解范围的数据进行有意义的处理。因此，人工智能对医疗保健的承诺是明确的-它可以根据患者的所有已知信息，结合集体经验的教训，使每一种诊断和治疗个性化。通过简化工作流程，提供常规、非严重疾病的自动化诊断，并从键盘中解放出来，人工智能最终还可以为医疗保健专业人员提供“时间礼物”-为患者提供最佳护理所需的专门时间。我对这个奖学金的基本愿景将是推动NHS和全球人工智能医疗保健的发展和应用，将眼科作为其他医学专业的典范。培训和认证这个奖学金的更新将使我能够大大提高我作为临床人工智能领导者的地位，巩固我领导多学科研究小组的领导能力和技术技能，建立国际网络，推动创新。CASE FOR CAMENTOFLF的更新将支持一系列相互关联的研究项目，这些项目涵盖了临床AI的广泛领域，从“从想法到算法”和“从代码到临床”。我的团队的早期工作的中心焦点将是我们最近为眼科开发的基础模型（“RETFound”）的缩放和验证。通过从200万到2000万张图像的训练，我们将创建一个模型，该模型可用于不太常见的视网膜疾病，并且在不同的人口群体中表现良好。我的团队还将使用人工智能来更多地了解最常见的威胁视力的视网膜疾病，例如年龄相关性黄斑变性（AMD）和糖尿病视网膜病变。我们将开发能够预测疾病进展、治疗负担和视力结果的系统，从而实现更好的治疗并减少视力丧失。我们还将继续探索新兴的“眼组学”领域-使用人工智能来预测阿尔茨海默氏症，中风和心脏病发作等系统性疾病的未来发展。与此同时，我的团队将探索从我们早期探索性工作中确定的最有前途的人工智能系统的临床验证和翻译。这将涉及对诊断准确性和临床安全性的评估，与监管批准和随后提供卫生服务的要求密切相关。潜在的应用和好处该奖学金的更新将通过进一步开发眼科人工智能系统使研究界受益，使其开源或与行业合作伙伴合作探索商业化。同时，更新将允许开发新的验证方法，包括计算机模拟和临床研究。通过创建基准数据集，它将协助监管机构在人工智能系统在真实的世界中实施之前确保其安全性和有效性。最重要的是，这些系统最终将为患者提供更好的眼科疾病诊断、治疗和监测，以及潜在的全身性疾病筛查。最后，NHS将受益于减轻已经过度紧张的医院眼科服务的压力，降低患者不必要地丧失视力的风险。