Artificial intelligence for multiple long-term conditions (AIM): a consensus statement from the NIHR AIM consortia

Abstract

Recent advances in causal machine learning and wider artificial intelligence (AI) methods could provide new insights into the natural histories and potential
prevention of clusters of multiple long-term conditions or multimorbidity (MLTC-M). When combined with expertise in clinical practice, applied health research and social science, there is potential to systematically identify and map new clusters of disease, understand the trajectories of patients with these conditions throughout their life course, predict serious adverse outcomes, optimise therapies and consider the influence of wider determinants such as environmental, behavioural and psychosocial factors. The National Institute of Health Research (NIHR) recently funded multidisciplinary consortia to bring together AI specialists, experts in big data and MLTC-M in the first and second waves of this new programme. The so-called AIM consortia of researchers will spearhead the use of artificial intelligence methods and develop insights for the identification and subsequent prevention of MLTC-M. This consensus agreement is aimed at facilitating a community of learning within the AIM consortia, promoting cooperation, transparency and rigour in our approaches while maintaining high methodological standards and consistency in defining and reporting within our research. In bringing together these research collaborations, there is also an opportunity to foster shared learning, synergies and rapidly compare and validate new AI approaches across our respective studies. This step is critical toimplementation on the pathway to patient and public benefit.

More information

Identifiers

ORCID for Hajira Dambha-Miller: orcid.org/0000-0003-0175-443X

ORCID for Simon Fraser: orcid.org/0000-0002-4172-4406

Catalogue record

Export record

Altmetrics