I-AIM: Individualised Artificial Intelligence for Medicine

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Management and treatment of complex, chronic diseases such as Alzheimer's disease is one of the biggest challenges facing modern medicine. All clinical trials of investigational treatments for slowing or stopping the progression of Alzheimer's disease since 2003 have failed. This is likely due to the complexity and duration (decades) of Alzheimer's disease, coupled with the highly individual nature of the disease and its progression. Combined, this works against clinical trials by making it extremely difficult to identify and recruit a large group of individuals who are at the same stage of the same trajectory, and so who might benefit from a potential treatment. In principle, this challenge can be met by a set of modern computational approaches called data-driven disease progression modelling (D3PM), but some technological development is required first.D3PM aims to combine statistics with the latest developments in AI and data science to estimate disease signatures that describe how a progressive disease plays out from beginning to end. This active research field grew from basic supervised machine learning (pattern learning/recognition) to a range of phenomenological (top-down) models, and mechanistic (bottom-up) models that incorporate a range of AI tools including unsupervised machine learning (pattern discovery). D3PM signatures have shown promise for estimating severity and predicting progression in neurodegenerative diseases such as Alzheimer's disease, but they currently lack in individual level precision, and mechanistic rigour.This research and innovation project is a unique combination of technology development and translational product development: a series of novel technological developments for individualising D3PM and expanding mechanistic modelling; and translational efforts to develop drug-development tools based on this next-generation technology. In combination, this work will speed up drug-development by increasing the efficiency of clinical trials: recruiting smaller cohorts of suitable individuals will reduce costs and lead to fewer false-negative results - where a drug works on a fraction of the population, but the trial cannot detect it because the majority did not respond to treatment. The chosen application is Alzheimer's disease, but the ideas are fit-for-purpose for similarly complex, progressive diseases.This fellowship is a significant launchpad for my career. My ambition is to benefit patients and society by providing robust computational solutions to complex healthcare challenges. My vision for achieving this ambition starts by targeting the global epidemic of dementia, where I have identified an unmet need (improving clinical trials) and proposed a viable solution in the form of this research and innovation project. The fellowship provides essential resources to capitalise on my recent progress in the field and to personally develop into a UK-based future leader in using AI for medicine and health.