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

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.