Augmenting AI: Increasing Throughput, Quality and Validity of Imaging Data for Biomedical AI

人工智能(AI)在生物发现和数字医疗中的使用正在加速增长。数字成像以适合广泛采用和自动分析的格式提供大量诊断数据。因此，出现了研究和商业机会，以增强和调整当前技术，以提高人工智能的效率和自动化。然而，为了确保在数字时代最安全和最可靠地部署这些技术，有一项核心要求是确保对其进行自动分析的数据具有最高质量和有效性，以确保可靠的积极结果。此外，为了保证实现自动化的最大好处，分析设备必须以最高的吞吐量和效率执行-这一过程可以通过集成AI-工作流和Industry 4.0方法来自我实现。为了增强生物医学AI，申请人提出了一个项目组合奖学金，将增强、整合和优化FFEI在生物成像和数字分析工作流程方面的过去、现在和未来的技术。这个雄心勃勃的项目将开发四项核心技术，每一项都加强了人工智能成像管道的一个阶段。技术将从市场准备的不同阶段开始，以确保商业和赠款交付成果是可管理和实现的。重叠的发展阶段将导致商业和研究活动的可持续平衡，同时逐步为人工智能成像市场做好准备，以迎接来自FFEI的模块化、端到端人工智能技术的出现，这些技术可以提供适用于数字医疗市场大多数细分市场的解决方案。一个长期目标是将所有的核心开发整合到FFEI的智能实验室产品中，在该产品中，一个单一的模块化设备可以执行生物医学AI实验室的所有基本活动。这位研究员的目标是通过建立一个研究环境来收集当前技术的基线指标，作为增强的起点，以此来发展FFEI的生物医学能力。该项目将建立新的和已建立的FFEI技术的原型，灵活地整合来自学术合作伙伴网络的新兴概念。最终，该研究员的团队将能够动态测试生物、机械和计算概念，以更好地实现人工智能图像数据的端到端优化。一个关键的目标是证明增强有效性在精炼端到端医疗人工智能的效率和可靠性方面。为了在医学上验证这些超越概念的技术，研究员将在FFEI生产的同时与NHS的合作伙伴合作，允许迭代优化和用于认证的案例数据。利用人工智能增强工作流程将需要实际评估和专家咨询，因此该研究员将创建并领导一个由学术和NHS合作者组成的联盟，该联盟拥有生物医学研发、诊断和人工智能分析方面的专业知识，通过传播同行评审的数据进一步提高人们的意识。该项目成功的一个重要组成部分将是在该研究员的领导下创建一个新的“人工智能成像”团队。FFEI拥有一支经验丰富且成熟的研发成像团队，该研究员将招募新成员来发展FFEI的生命科学业务，以支持这一团队并探索新概念，同时学习将创新思维与商业应用相结合的技能。作为回报，新团队将为FFEI带来新的人才，预计将招聘人工智能软件、先进的光学机械和生物专家，发展一支团队，将FFEI带入人工智能增强和商业成功的新时代。在FFEI执行团队的指导和支持下，该项目将使FEI研究员在商业管理、团队领导和商业合作方面获得个人发展机会。

The use of artificial intelligence (AI) in biological discovery and digital healthcare is increasing at rate. Digital imaging provides large quantities of diagnostic data in formats amenable to widespread adoption and automated analysis. As a result, research and commercial opportunities are arising to enhance and adapt current technologies to improve efficiency and automation with AI. However, in order to ensure the safest and most reliable deployment of these technologies in the digital era, there is a core requirement to ensure that the data upon which automated analyses are performed are of the highest quality and validity to ensure reliably positive outcomes. Furthermore, to warrant the maximum benefits of automation are reached, analysis devices must perform at the highest throughput and efficiency - a process that can be self-fulfilling by the integration of AI-workflow and Industry 4.0 approaches.To augment biomedical AI the applicant proposes a Portfolio Fellowship that will enhance, integrate and optimise FFEI's past, present and future technologies in bio-imaging and digital analysis workflow. This ambitious project will develop four core technologies, each enhancing a stage of the AI imaging pipeline. Technologies will start from different stages of market-readiness to ensure commercial and grant deliverables are manageable and realised. The overlapping stages of development will lead to a sustainable balance of commercial and research activities, whilst incrementally priming AI imaging markets for the emergence of modular, end-to-end AI technology from FFEI that can provide solutions that are adaptable and integrative to most segments of the digital healthcare market. A long-term objective is to integrate all the core developments into an FFEI 'smart lab' product, in which a single, modular device can perform all essential activities of biomedical AI laboratories. The Fellow aims to develop FFEI's biomedical capabilities by establishing a research environment to collect baseline metrics of current technology as a starting point for enhancement. The project will prototype new and established FFEI technology with flexibility to integrate emerging concepts from a network of academic partners. Ultimately, the objective will be for the Fellow's team to be able to dynamically test biological, mechanical and computational concepts to better achieve end-to-end optimisation of image data for AI. A key objective is to prove augmentation validity in refining end-to-end medical AI efficiency and reliability. To medically validate these technologies beyond concept, the Fellow will collaborate with NHS partners in parallel to FFEI productisation, allowing for iterative optimisations and case-data for accreditation. Enhancement of workflow processes with AI will require practical assessment and expert consultation, therefore the Fellow will create and lead a consortium of academic and NHS collaborators with expertise in biomedical R&D, diagnostics and AI analysis, further raising awareness through dissemination of peer-reviewed data.An essential component of the project's success will be the creation of a new 'AI imaging' team under the Fellow's leadership. FFEI have a highly experienced and established R&D imaging team into which the Fellow will recruit new members to grow FFEI's Life Science business, to bolster this team and explore new concepts whilst learning the skills of blending innovative thinking with commercial application. In return, the new team will bring fresh talent to FFEI, with anticipated recruiting of AI software, advanced opto-mechanical and biological experts, developing a team to take FFEI into a new age of AI augmentation and commercial success. The project will benefit the Fellow with personal development opportunities in business management, team leadership and commercial collaborations, under the mentorship and support of the FFEI executive team.