I-AIM: Individualised Artificial Intelligence for Medicine

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.

I-AIM will impact the following important challenges related to Alzheimer's disease:

1. Discovery of precision treatments and novel treatment targets
Discovery of disease-modifying therapies in Alzheimer's disease in clinical trials has been hampered by heterogeneous cohorts of individuals at different stages of different trajectories within a disease, that obscures even strong effectiveness of a treatment in a subgroup. I-AIM will impact upon clinical trials by individualising data-driven disease progression modelling to provide clinical trials with homogeneous cohorts of individuals who are all at a similar stage of a similar trajectory, thereby empowering the discovery of precision treatments in the medium term.
I-AIM develops new computational models of disease mechanisms that reveal novel targets for future treatment development in the longer term.

Beneficiaries include patients (and future patients), pharmaceutical companies, clinicians, and society as a whole.

2. Precision patient care
I-AIM will enable precise characterisation of a patient's disease trajectory and their position on it. This precision empowers patient management and care decisions through, e.g., accurate prognosis, and differential diagnosis; which enables patients to be referred to get the support, and eventually the treatment, they need. These technological developments in I-AIM will impact upon the development of new workflows for clinicians informed by data-driven solutions.

Beneficiaries include patients and their families and carers, clinicians, and (long term) medical technology companies.

In the medium-to-long term, the technological developments in I-AIM promise considerable socio-economic impact through informing and improving clinical trials into putative disease-modifying therapies for Alzheimer's disease. Dementia, of which Alzheimer's disease is the primary cause, represents a global crisis in healthcare. Annual socioeconomic costs in the UK alone exceed £26 million (Alzheimer's Society 2014), including costs of care and lost productivity. Costs are rising rapidly with the ageing population. I-AIM's direct impact on drug-development efforts will decrease the mammoth socio-economic costs of dementia. For example, an Alzheimer's disease treatment that prolongs independent life by two years could save up to £12.9 billion per year by 2050
(Lewis et al., ARUK 2014; dementiastatistics.org). Such a treatment relieves untold personal suffering for vast numbers of Alzheimer's disease patients, their families, friends, and carers. There is also potential for vast economic impact to come from two revenue streams: increased productivity across society through prolonged independent life (and reduced burden on the healthcare system); and revenue from drugs licensed by the pharmaceutical industry. The latter revenue stream is expected to generate significant interest in I-AIM from the private sector, who will be engaged from the offset and throughout the project.

The scale of Alzheimer's disease and the novel, yet attainable, solutions proposed in I-AIM promise profound impact for society all the way from patients to the private sector and the wider public. These impacts are expected to entreat policy makers in Health and elsewhere to strongly consider data-driven AI solutions to complex problems.

I-AIM's impact on research and society will be expedited through wide communication and dissemination of results via open-access routes wherever possible and appropriate. This will be balanced by giving due consideration to commercialisation opportunities that will impact upon the private sector initially, such as drug-development tools, for which expert collaboration will be sought with the primary beneficiaries: pharmaceutical companies.